{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real-world data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The section reports the performances that are obtained on real-world data using imbalanced learning techniques. The dataset is the same as in [Chapter 3, Section 5](Baseline_FDS_RealWorldData). Results are reported following the methodology used in the previous sections with simulated data. \n",
    "\n",
    "We first report the performances for cost-sensitive techniques, varying the class weight for decision trees and logistic regression models. We then report the performances for resampling techniques using decision trees, and varying the imbalance ratio with SMOTE, RUS, and a combination of SMOTE and RUS. We finally report the results using ensemble techniques, varying the imbalance ratio with Bagging and Random Forests models, and varying the class weight with an XGBoost model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Initialization: Load shared functions\n",
    "\n",
    "# Load shared functions\n",
    "!curl -O https://raw.githubusercontent.com/Fraud-Detection-Handbook/fraud-detection-handbook/main/Chapter_References/shared_functions.py\n",
    "%run shared_functions.py\n",
    "#%run ../Chapter_References/shared_functions.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cost-sensitive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We followed the methodology reported in Section 6.2 ([](Cost_Sensitive_Learning_Transaction_Data)), saving the results in a `performances_cost_sensitive_real_world_data.pkl` pickle file. The performances and execution times can be retrieved by loading the pickle file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filehandler = open('images/performances_cost_sensitive_real_world_data.pkl', 'rb') \n",
    "(performances_df_dictionary, execution_times) = pickle.load(filehandler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree\n",
    "\n",
    "The results for decision tree models are reported below. The tree depth was set to 6 (providing the best performances as reported in [Chapter 5](Model_Selection_RWD_Decision_Trees)). We varied the class weight in the range 0.01 to 1, with the following set of possible values: $[0.01, 0.05, 0.1, 0.5, 1]$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "performances_df_dt=performances_df_dictionary['Decision Tree']\n",
    "summary_performances_dt=get_summary_performances(performances_df_dt, parameter_column_name=\"Parameters summary\")\n",
    "\n",
    "get_performances_plots(performances_df_dt, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Class weight for the majority class\",\n",
    "                       summary_performances=summary_performances_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recall that a class weight of 1 consists in giving the same weight to positive and negative classes, whereas a class weight of 0.01 consists in giving 100 times more weight to the positive class (thus favoring the detection of fraud instances). We also note that a class weight of 1 provides the same results as in [Chapter 5](Model_Selection_Decision_Tree). \n",
    "\n",
    "The results show that decreasing the class weight allows to increase the performances in terms of AUC ROC, but decreases the performances in terms of Average Precision and CP@100, particularly for very low values (close to 0.01).\n",
    "\n",
    "The performances as a function of the best parameters are summarized below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.871+/-0.01</td>\n",
       "      <td>0.067+/-0.02</td>\n",
       "      <td>0.218+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.881+/-0.02</td>\n",
       "      <td>0.066+/-0.01</td>\n",
       "      <td>0.183+/-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.881+/-0.02</td>\n",
       "      <td>0.068+/-0.01</td>\n",
       "      <td>0.232+/-0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters          0.01               0.1                0.5\n",
       "Validation performance     0.871+/-0.01      0.067+/-0.02       0.218+/-0.02\n",
       "Test performance           0.881+/-0.02      0.066+/-0.01       0.183+/-0.04\n",
       "Optimal parameter(s)               0.01               1.0                1.0\n",
       "Optimal test performance   0.881+/-0.02      0.068+/-0.01       0.232+/-0.04"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results follow the same trends as those obtained with the [simulated data](Cost_Sensitive_Learning_Transaction_Data): Cost-sensitive learning is effective for improving AUC ROC performances, but detrimental to Average Precision. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression\n",
    "\n",
    "The results for logistic regression are reported below. The regularization parameter C was set to 0.1 (providing the best performances as was reported in [Chapter 5](Model_Selection_RWD_Logistic_Regression)). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pyeizzz7LG2+84bOdnTt3MnfuXLp06cK4ceP4/PPPadCgAd98843Pci1atCArK4ukpCTvtF9//RW73S5jWQhRToZhEBcXxwcffFCkxXPr1q18/vnn1KtXj+3bt/PHH3+wZMkSHnzwQbp168bx48dLvXDVqlUrn1s1Dh8+THZ2dqXWf7EddyIjI72BOCQkxCf8vvPOO2iaxp///GcyMzOZM2cOI0aMKNf3oUOHDuzcudP7/0Mpxc6dO723GXXo0IEdO3Z4lz9x4gTHjx/3zheioiRo13IJCQmEh4cTHx9PdHS0998NN9xAp06dWL16NbGxscTExPD444/zv//9j6NHj/L1118zbtw4br/9dho3bgzAJZdcwt///ncmTpzIO++8w6FDh9izZw9PPvkk+/btq/BzJJ977jkyMjL417/+BXgGnJg6dSqzZs3ijTfeICkpicOHD/Puu+/y3HPP8eSTT3pPXiu7FiFE1SnPcQjwtmpfc8013q7YLVu2pEePHjz99NPs3r2bAwcOMH78eJKTk4sMWlMgMjKSzZs3c/ToUXbs2OEdgMfhcBASEsKgQYN4+eWX2bVrF7t27eKll14CPCPQns/+cnJymDRpEklJSaxYsYINGzZwzz33FLtsWdsfMGAAWVlZTJ8+nYMHD5KQkEBCQgI9evTw2U5gYCBz585l2bJl/P7772zatIkTJ07Qtm1bn+VatGjBTTfdxPjx4/nxxx/58ccfvSMPF1xEFUKU7W9/+xt5eXmMGjWKrVu3cuTIEVavXs0TTzzBoEGD6Ny5M5GRkeTk5PCf//yH33//nZUrV7J06dJS7/G95557+OCDD/j888/5+eefef7554sd6PVCXGzHndatW/P9998D0K9fP+bOncumTZuYOXMmb7/9NlarlW+++YYRI0bQvXv3InWW5JZbbiE7O5sXX3yRX3/91XuhoX///gDEx8eTkJDAihUrSExMZPz48fTs2ZMrrriiXNsXoojqfZqYqG633HKLmjJlSrHz1qxZo6Kjo1ViYqJKSUlREyZMUN27d1dt2rRRN910k/rHP/6h8vLyiqy3du1aNWTIEBUbG6u6du2qRo8erRITE0usobTnaM+cOVO1bt3aZ/0ffvhBPfjgg+qaa65RsbGx6t5771WbNm0qdv2K1iKEqH7lPQ4dP35cxcTEeJ9pWiA5OVmNHTtWxcbGqs6dO6vHHntMnTp1Siml1NatW1V0dLRyOp3e5X/44Qd12223qXbt2qnevXurBQsWqLvvvlu9+eabSiml7Ha7euqpp1THjh1V9+7d1fz581V0dLT3Wayl7e9cn3zyierevbt6/fXXVceOHVXfvn3VunXrvPPHjx/v81zW8mx/165dasiQIapt27bq5ptvVp999lmxn/XTTz9Vt9xyi2rbtq3q1auX+uCDD5RSSh09etTn2a+pqalq7NixqlOnTqpLly5q/PjxKi0trcTvX3E1CyGU+uOPP9TEiRNVz549Vbt27dStt96q3n77beVwOLzLzJ49W1177bWqU6dOaujQoeqTTz5R0dHR6tixY8X+viml1Lvvvquuv/561aVLF7Vo0SLVuXPnCj9Hu7CL/bhjt9vVNddco3bv3q3S0tLUQw89pNq3b6+eeOIJtXXrVnXNNdeo66+/Xs2bN0+53W7122+/qaysLJ/Pe249BXbv3q3uvPNO1bZtWzV48GC1Z88en/mrVq1SN954o+rYsaN65JFHVHJycqnfayFKoyklN2IJIYSoOzZu3Mh1113nHRX4xx9/ZPjw4ezcubPCt5ysWrWKWbNmFek6KYQQ4vytWLGCuXPnsnDhQu8zys/ldrt5/fXX2bRpE6tXryY4OLiaqxSidDIYmhBCiDrlzTffZNOmTTz88MNkZWXx2muv0atXLxnXQQgh/MTdd99NRkYGQ4YMIS4ujl69enHFFVcQHBxMSkoKO3fuZMWKFVitVt59910J2cIvSYu2EEKIOuXXX3/lxRdf5Mcff8Rms9GrVy8mTJhQ5jNyiyMt2kIIUXWSkpL46KOP2LZtG8eOHSMvL4+IiAiuuuoq+vfvz4ABA+QiqfBbErSFEEIIIYQQQohKJKOOCyGEEEIIIYQQlUiCthBCCCGEEEIIUYkkaAshhBBCCCGEEJVIgrYQQgghhBBCCFGJJGgLIYQQQgghhBCVSIK2EEIIIYQQQghRiSRoCyGEEEIIIYQQlUiCthBCCCGEEEIIUYkkaAshhBBCCCGEEJVIgrYQQgghhBBCCFGJJGgLIYQQQgghhBCVSIK2EEIIIYQQQghRiSRoCyGEEEIIIYQQlUiCthBCCCGEEEIIUYkkaAshhBBCCCGEEJVIgrYQQgghhBBCCFGJJGgLIYQQQgghhBCVSIK2EEIIIYQQQghRiSw1XcCFcLvduN3uYucZhlHivJrmr7X5a13gv7X5a11QvtpsNls1VSPOlxznKpe/1gX+W9vFXpcc5/yfHOcql9RVcf5amxznLm4XfdBOTk4udl5UVFSJ82qav9bmr3WB/9bmr3VB+Wpr0qRJNVUjzpcc5yqXv9YF/lvbxV6XHOf8nxznKpfUVXH+Wpsc5y5u0nVcCCGEEEIIIYSoRBK0hRBCCCGEEEKISiRBWwghhBBCCCGEqEQX9T3aQgghhBBCCCHOj9Pp5MiRI+Tm5tZ0KRedwMBAmjZtitVqLXa+BG0hhBBCCCGEqIOOHDmCYRg0atQITdNqupyLhlKKrKwsjhw5QsuWLYtdRrqOCyGEEEIIIUQdlJubS0hIiITsCtI0jZCQkFJ7AkjQFkIIIYQQQog6SkL2+Snr+yZdx4UQooJM02TKlCkkJiZis9mYNm0azZo181kmJyeHUaNG8dJLL9GyZUucTicTJkzg2LFjOBwOxowZQ+/evWvoEwghhBBCVI3Zs2eTmJhIcnIyeXl5XHrppURGRjJt2rRqryUvL48vvviCAQMGVPu+JWiXRSk0ux1cLlRYGFjkWyZEXbdx40YcDgfLly9n165dvPLKK8ybN887f8+ePUyePJmTJ096p3322WdERkby2muvkZqaysCBAystaGt2OyooCAyjUrYnhBD+JiUnBafpBEDXdDRNo+A/XdcxMAiwBGAzbDVcqRDiscceA2DdunUcOXKEMWPG1FgtKSkprF27tvYE7bJaez777DPeffdddF1n8ODBDB8+HKfTyTPPPMOxY8fQdZ0XX3yxxBvLq4VSaFlZ6MnJ4HKBpkFKCmb9+hK4hajjduzYQY8ePQDo2LEje/fu9ZnvcDiYM2cOTz/9tHfaLbfcQr9+/bzvjUoMxVp2NspikaAthKi1Mh2ZKJS3q6ZSCoXyzjeViVIKq2El3BZOkCWIACNAusQK4SfsdjujRo1i2bJlGIbB3LlzufLKK1m1ahVNmzblyJEjKKWYOnUqUVFRzJs3j927d2OaJsOGDaNXr14+25s2bZq3l2B8fDx9+vRh586dLFy4EF3Xueyyy3j66adZvHgxhw4d4p133uH++++v1s9cJWmxrNaeV199lYSEBIKDg4mLiyMuLo7vv/8el8vFsmXL2LJlC7NmzWL27NlVUV7plELLyUE7fRrN5UIFBEBAgGeeaaKnpkJqKma9eqjwcDmxFaIOstvthIaGet8bhoHL5cKSfwGuc+fORdYJCQnxrvv444/zxBNPVF5Bbjea213olFMIIWofm2FD10ofXshlukjLSyMlNwUNjTBbGCHWEAKMAAxdztmEfyq4cGQqE4Xyvs915aKUqhUXjEJDQ2nfvj3btm2ja9eubN26lYceeohVq1bRrl07nn76aVatWsX777/Ptddey4kTJ5g/fz55eXn89a9/5eqrryYsLAyArKws/ve///H222+jaRrbt29HKcWMGTOYN28e9erVY+HChaxfv56RI0eSlJRU7SEbqihol9XaExMTQ2ZmJhaLxfvD07x5c9xuN6ZpYrfbvSes1aYgYCcnozkcKJsNlX9i7KXrqOBgCdxC1HGhoaFkZWV535umWa5j1okTJ3j00UcZPnw4t99+e7n2ZRgGUVFRxc6zWCyeeTk5EBwM9euX7wNUA29tfsZf6wL/rU3qqv3K6omYkJDA4sWLMQyD6OhopkyZwpo1a1i9ejXguQfyp59+YsuWLRw9epTRo0dzxRVXABAfH0///v2r7bNYdAsW3XM8NpVJljOLjLwM0CDIEkSgEYiu6+jo6JrnHxqeLuia7umMnt8tvfB7IeBsIPZ+LQjHhd6jPD97pjIxMTFNz2u3cp99X/irMs9uH4XG2Z+3TD2TUHcogZbAmvi4lW7AgAF8/PHHKKXo0qWL9/nTBQ0Ubdu25dtvv6VRo0YkJibyt7/9DQCXy8Uff/zhDdohISGMHTuWV199laysLPr160daWhpnzpxh4sSJgOe4dPXVV9fApzyrStJsWa09rVq1YvDgwQQFBdG3b1/Cw8PJysri2LFj3HrrraSmpjJ//vyqKK142dnoycloeXmogICiAftchQN3SooncBd0KZfALUStFxsby+bNm+nfvz+7du0iOjq6zHXOnDnD/fffz6RJk7juuuvKvS+3201ycnKx86KiokhOTkZPSUFlZKCU/7RpF9Tmb/y1LvDf2i72upo0aVIN1VzcSuuJmJuby6xZs1i7di1BQUGMHTuWzZs3M2jQIAYNGgTACy+8wODBgwkPD2f//v2MGjWqRlqPzqVrujegKKVwmk7S3ene96qUfkDnBh6LZkHT8wM4OhbdgivARVpOGhbd4g3umqb53D+uaYUCe0F4l+BeLUpqJVZKYWLiycSe95ZcCyk5KZh4QnNBSHaZLs8ypmcdt3L7/FyAZxt6/oOcCv9MFb5IU/jCTcGFHYtuKfPnQdM1cFfBN6eGdOjQgTfeeIOEhAQeeugh7/TExEQaNWrEnj17aN68OU2bNiU2Npbx48djmibvvfcel156qXf5M2fOkJiYyMsvv0xeXh6DBg3i5ptvplGjRrzyyiuEhoby7bffEhwcjKZpNXZ+VCVBu7TWngMHDvDVV1/x5ZdfEhwczLhx4/j888/ZtWsX3bt356mnnuLEiROMHDmStWvXElDQbbsY5WrpKY3DAadOQXY2REaC7TwH0DBNzzbS06FBAyijhdtfr8L7a13gv7X5a13g37Vd7Pr27cuWLVsYNmwYSimmT5/O2rVryc7OZujQocWuM3/+fDIyMpg7dy5z584F4K233iIwsHKuUmt5edJ1XAhxXkrriWiz2Vi2bBlBQUGAp2Wp8LnZnj17+PXXX5k8eTIAe/fu5eDBg3z55Zc0a9aMCRMm+DS+1BRN0857oLRzWzFNZZLnziPbmY3dYS/SwlkQqsE3eBUO77qW36qun/1q0SyeYI6OoRsYmnE2tJ/ztXBre1nd6f1ddbcSa+QHr0L51mFzkO5I9/1eF3x/0dGMs+/Fhbn55pvZtGkTLVq08E5bv349y5YtIzAwkEmTJhEeHs7OnTsZM2YMOTk59OzZ03sLHpy90HrfffcRFBREfHw8VquVv//974wbNw7TNAkJCWHixIkEBwfjdDqZO3cujzzySLV+1ioJ2qW19oSFhREYGEhAQACGYVC/fn0yMjIIDw/3dh+IiIjA5XLhdpd+Cac8LT3FUgotMxP91CnPAEKBgZ4BzwpdHDgvpon2yy+g654u5SW0cF/srQM1wV9r89e6oHy1SUvP+dF1nalTp/pMK27wxiVLlnhfP//88zz//PNVVpPmdIJSnoEbhRCiAkrriajrOg0aNAA8x7Ts7Gyuv/5677ILFizg0Ucf9b5v3749Q4YMoW3btsybN485c+Ywfvz4UvdfnoaTdD29XPdoVyeLxUJAw5IbhEpT0GpaUgurW7lx4fKEwYKsXvBa4RMSAW83eF3TcWQ7UFaFpmsYmiewF8wzdKNoS3vh8K6VHih9WomLuQDh02qsPC3CBQH4dM5p8qx5PmG4IDh7P19hBZ+30PegcPg1NAObZvMJxefba8BisRBiK6NHaw3IdecSFRV10XYdj4uLKzLN7XYXGQF89OjRRR6T+vjjj5e4XU3TfAacLdC1a1e6du1aZPrixYvLW3KlqpKgXVZrz9ChQxk+fDhWq5WmTZsycOBA7zNmC0Ygf/p3YcQAACAASURBVPLJJwkODq784pxO9DNn0LKyPN2/9Uo8YOu6p9u5aXpGKy+4h1u6lAshqprb7fknT0QQQlRQWeNOmKbJa6+9xsGDB5k9e7Y3xGRkZPDbb79x7bXXepctuCWw4PWLL75Y5v7L03CSlp6G1bD6VdCuV68eqampNV0G4BuAIyMjSU1N9ek2Dfi08ha3fuFwqmkaBgaarnm7TZfWSpw/0Scsn9sSX69ePdJT04ttpa9p/vT/sjBrqJXk5OQyg/bF0nAybdo00tPTmT59ek2XUi2q5IysrNae+Ph44uPjfebbbDbeeOONqijHS7PbPa3Ymoaqym5MxQXugnu4KzPYCyFEPk0pT88cCdpCiAoqa9yJSZMmYbPZmDt3Lnqh85jvv/+ebt26+Sz7wAMPMHHiRNq3b89///tf2rRpUy2foa4r3JprNaxYDesFba9wyzQ6lXJveYARcMF1iYtbcT373nzzzRqopHrUjTMylwstORk9I8PTil1drcsFgdvtRj9zxvMc7qgoqFevevYvhKgzlFJopin3aQshKqy0noht27bl448/pkuXLowcORKAv/zlL/Tt25eDBw9y+eWX+2xrypQpvPjii1itVho0aFCuFm3hfwqCe5Hu3EKIcqv9QTs3F+PECU8rdv6Q8NXOMM4G7tOnPfdy67qnVV1auIUQlUHXPQM8VsUtN0KIWq2snogHDhwodr0HH3ywyLQ2bdqwbNmyyi1QCCEuQrU+5Wl2u6eFp5JG9r0gBYHbZkM/fRrjyBG0jAzPqOVCCHEhDMMTtIUQQgghRI2r9UEb8L9RePMDt7Ja0U+d8gTuzEwJ3EKI86Z0HS0vr6bLEEIIIYQQ1IWu4/7MMDzdx91u9JMnwWLBjIrytHpLl3IhREVYLJ5HfAkhhBBCXERmz55NYmIiycnJ5OXlcemllxIZGcm0adPKXDcpKYnMzEw6duxYDZVWjARtfyCBWwhRGZTyPOJLHicohBBCiIvEY489BsC6des4cuQIY8aMKfe6X331FfXr15egLcpQELhdLvRTpzyBu359zzR/6/4uhPA7CjyP+JKgLYQQQoiLlMvl4rXXXuPo0aMopXjooYeIjY1lwYIF7NixA6UUffr0oVevXqxfvx6r1UpMTAytW7eu6dJ9SND2RxYLymI5G7hTUiRwCyHKpCmF5nbLI76EEEIIUWHLDyznw58+rNRtDr9qOEOvHFqhddauXUtERATPPvss6enpPPLIIyxdupQNGzYwZ84cGjRowPr162nYsCH9+/enfv36fheyQYK2fysucBd0KZfALYQ4l657WrSFEEIIIS5SSUlJ7N69m/379wPgdrtJT0/nhRdeYP78+aSkpHDttdfWcJVlk6BdDm7TjVu5segWdK0G7pkuHLjPvYdbArcQIp8yDJCRx4UQQghxHoZeObTCrc9VoVmzZjRs2JCRI0eSl5fH4sWLCQoKYtOmTbzwwgsopbj33nvp06cPmqahlH/25ZOgXQKlFHnuPOxOO1nOLBQKDQ1DMwgwAgi0BGLRLVh1K4ZmoFVH4D0ncCuLBdWgASo4WAK3EALyH/Hln39uhBBCCCHKdscddzBjxgweffRRsrKyGDRoEDabjfDwcO677z7CwsK45ppraNy4MVdeeSVz5syhWbNmdO7cuaZL91Hrg7aJAtNNeYcGcpkusl3ZZORl4FZudE0nyAjyBlnTdJPnziPHleN5r0x0TceqWwm0BBJgCcCiWTA0A0OvogGJCgK304l+4gTKakU1bIgKCpLALURdZrGg5ebWdBVCCCGEEBUWFxfnfT1x4sQi8++//37uv/9+n2ndunWjW7duVV7b+aj1QTstNw171nEsZihBRhCB1kAsmqcluqAVWilFrjuXTEcm2a5sdE3HptmwGQFFtqfrBrZzY7tSuJSLTGcm6Y50NDzbLan1u9JYrSir1TdwN2gggVuIukrTPI/3kkd8CSGEEELUqFoftBUKQzPQ0clyZZHhzPAG4QA9AJthI8uVhalMDM0g2BJS8Z1oGhbNigWrz2TTdONwO8hx5aCUQqHQNZ1cay45uTkEWgIxMLDolgtr/T43cNtsqKgoCdxC1EGqIGxL0BZCCCGEqDG1PmgXMHQLRuGPm98KneXKwqbb0KpgkDNdN9AxfOO3UpjKxO60k+nI9ExCYdEs2IwAAi0BWHXr+d37LYFbiDpPA8/I4zZbTZcihBBCCFFn1ZmgXUQJrdDVsV+rbiXQCPKZrJSJ0+0gN7/1Gw00NCy6xXPvt+G597tcrd+FA/fx46iAAMyoKJDALUSdoLlcMiCaEEIIIUQNqrtB289omo7VsBVp/XYpF1nOLG/rN3ju/bYZNgItgVh1qzeAF2n9LhS4DQncQtQJyjDA4ajpMoQQQggh6jQJ2v6shFZ3T+u3k1xXLqpQu5WnpfzsyOfe1u/CgTu/S7kEbiFqKcOQR3wJIYQQQtSwyr8x2Z+43VjOJNd0FZWuoPU70BJEkCXY+09DI8uVxZmcM5zMPsnv9t85knmEU9mnyHBkkKO5cATaUEphnDiBfuwYZGfX9McR4qJjmiaTJk1i6NChjBgxgsOHDxdZJicnh2HDhpGUlFTudSqFYaA5nVWzbSGEEEKISvTII4+wY8cOn2mzZs3is88+K7Ls4MGDycvLY8mSJezfv99nXl5eHoMHDy51X59++ikul4uff/6Zd95558KLL0OVBO2yTig/++wzBg4cyODBg/nwww+90xcsWMDQoUMZNGgQK1euvOA6Av79b1oNjMeSmnbB27oYGLqFACOQIEuwN4QH6AE43U7S8tI4lX2KY1nHOJJ7gmMqnZSs0+Qe+gXn4d9wZ9lrunwhLhobN27E4XCwfPlynnrqKV555RWf+Xv27OGee+7h6NGj5V6n0ui6Z9Rx06ya7QshhBBCVJI77riDzz//3Pve6XSyZcsW+vbtW+I6I0aMoHXr1hXe1/vvv4/b7SY6OrrI87irQpV0HS98Qrlr1y5eeeUV5s2b553/6quvkpCQQHBwMHFxccTFxXHgwAF27tzJRx99RE5OTqVcZXhm5kwW5jkI+ekXci5rdsHbuxgVe+834DZdZOku7LpCy7RD8iGy046QFRhEQEikZ/A13dP9XK+CEdmFuJjt2LGDHj16ANCxY0f27t3rM9/hcDBnzhyefvrpcq9TmZRSMvK4EEIIIfzejTfeyIIFC8jNzSUwMJBvv/2W2NhYJk+eTF5eHhkZGYwaNYqePXt615k2bRp9+vShffv2vPDCC2RmZnL55Zd75+/cudObJXNzc5k4cSK7d+8mJSWFyZMnc/fdd7NmzRqmTp3KF198wYoVK7DZbFx++eWMHz+eL774gq1bt5Kbm8uxY8e45557iIuLq/Bnq5KgXdYJZUxMDJmZmVgsFpRSaJrGd999R3R0NI8++ih2u93nBPV8/RIcjAsISUwip0+fC95ebeLzuLPgIAiGAN1Czu/HyAo4SWpkGCowEACrYSXICCLQCMRiWDwDsOlye7+ou+x2O6Ghod73hmHgcrmwWDy/F507d67wOiUxDIOoqKhi51ksFs+8nBzfYG21QmSkZxyGGuKtzc/4a13gv7VJXUIIUTcELF9OYKHexpUhd/hw8oYOLXmfAQH06NGDr7/+mn79+rFu3TpiY2Pp168fsbGx7Nmzh0WLFvkE7QLr16+nRYsWPPzww+zbt8/bBf3gwYNMmjSJhg0bsnjxYjZv3szIkSN57733eOGFF9i3bx8A6enpvP3227z77ruEhITwxhtvsGbNGoKCgrDb7cycOZOjR4/y9NNP+0/QLuuEslWrVgwePJigoCD69u1LeHg4qampHD9+nPnz5/P7778zZswYNmzYULHnSJ9j0Ycf4ri1HyGJv3Lmgj9V7afbAtHDIghyOAg+nYkKdOKqH4nT0Ml2ZZPpzEQphUJhaAYBRgCBRiCBlkBp/RZ1SmhoKFlZWd73pmmWGZjPZx0At9tNcnLxY01ERUWRnJyMnpLi6Spu9fRd0bKzMQMCUIWOw9WtoDZ/4691gf/WdrHX1aRJk2qoRgghxPkaMGAAc+bMITY2lszMTK677joWL15MQkICmqbhdruLXe/gwYNce+21ALRp08Z7XtWwYUNmzZpFUFAQp0+fpn379sWuf/z4cZo3b05ISAjgaSDevn07rVu3plWrVgA0atQIx3k+zaVKgnZpJ5QHDhzgq6++4ssvvyQ4OJhx48bx+eefExkZSYsWLbDZbLRo0YKAgABSUlJKvVpdVkvPn/70J5yxHQlZ/wWRYWGeexf9gGExiIyIqOkyiihSl8OBZs/GDAlG1a+PCgzwzlJK4TSduEwXOSonfyLYDBtBliDPo8cMKxoauqajazqa5nmtoVX4Aoq/tlz4a13g37Vd7GJjY9m8eTP9+/dn165dREdHV8k650tpGuTlQQ0GbSHExcM0TaZMmUJiYiI2m41p06bRrNnZW+4SEhJYvHgxhmEQHR3NlClT0HWdO++8k7CwMAAuv/xyXn75ZQ4fPswzzzyDpmm0atWKyZMno/vJ+ZcQonR5Q4eW2vpcVVq2bEl2djYrV67ktttu46233mLAgAFcd911rFu3jvXr1xe7XrNmzdi7dy89evTg559/xuVyAfDKK6+wYsUKQkJCePHFFz231AG6rntfg+dC7KFDh8jJySEoKIidO3fypz/9CeCCGnsLVEnQLu2EMiwsjMDAQAICAjAMg/r165ORkUHnzp15//33GTVqFKdOnSInJ4fIyMhS91NWS8+016dxaeavjM60k73vJxxNLy922eoWGRFBWnp6TZdRREl1aaeT0Y6fwAwOxl0/EhUQUMzaHrlmLikqBZfpKnVfuqZj6AYamqc1XPM8B7zwa03T0PEE9IZRDUlNSfWGdX/hry09UL7apKXn/PTt25ctW7YwbNgwlFJMnz6dtWvXkp2dzdAS/kAVt06VsVjkWdpCiHIrbWyd3NxcZs2axdq1awkKCmLs2LFs3ryZ7t27A7BkyRKfbb388ss88cQTdO3alUmTJvHll1+WOqiREEIAxMXFMWfOHFatWkVQUBCzZs3i/fffp3HjxqSlFT+w9aBBg5g+fTpjxoyhadOmWPN79vXr14+//vWvhIWFUa9ePc6c8fRtbt++Pf/v//0/Ro0aBUBkZCQPPPAAjz32GJqmcfnllzNmzBg2btxYKZ9JU4VjfSUpuDL6888/e08o9+/f7z0J/eijj/jkk0+wWq00bdqUF198EZvNxquvvsq2bdtQSvHkk0967/MuicPhKDVot7u7HbZLf2bPPDg6aTxpN/eq7I96Xi62oF1Ay3OgOR2YISG460WUGrjLopTCVCYmJigwMb3d0k11drRkDQ2FIjIyktS0VDQ8V5cKgrqBga7rWDQLhnb29blBXdd0n9eVRYK2qGplHeeSk5M9j+or1HUc00RzOnE3q7lBIP31d8Nf6wL/re1ir0uOc2V7+eWXad++vfcexB49evDtt98CnnO6lJQUGjRoAMDjjz/O3XffTVhYGE8//TSXXXYZLpeLsWPH0rFjR3r06ME333yDpmls3LiRLVu2MHny5FL3X57j3OH0w1gNq19dbK9Xrx6pqak1XUYRUlfF+Wtt1lArIc4QAi2BpS53Ice5ffv20bhx4/Nev647efIkbdq0KXZelbRo67rO1KlTfaa1bNnS+zo+Pp74+Pgi61XGAGiFhaeGs7M95NkMgvYn+k3QvlipABsqwIaW58B6LL+F+zwDt6ZpGJonKJdHqC0Up/Xss4FN5QnmJiam28SBwxvQzw3qJqY3oCuUpxVds6DpGgaG995yQzM84V0zPH/INdAp1O290Gsh/JquewZHM02/uWVGCOG/ShtbR9d1b8hesmQJ2dnZXH/99fz888888MADDBkyhEOHDvHQQw+xYcMG7yC3ACEhIWRmZpa5//IM+piup2MzbH4VtC0WC/Xq1avpMoqQuirOX2vLdecSFRVVZtAW/qlWDx1tdVoJTg1h92VO2v6UWNPl1Bo+gfv345ihobgjI1AB1fcooYIgXN6gfq7CQT3PnedtSS9oVQdAeS4IKJTPiQOARfOcfORYc8jIyvCG84LQXhDIfe5Rz38tQV1UBwWe52lL0BZClKGswRpN0+S1117j4MGDzJ49G03TaN68Oc2aNfO+joyM5PTp0z73Y2dlZREeHl7m/ssz6GNaepq0aJeT1FVx/lqbNdRKcnJylbZoi6pTq4M2QOSJCL655Didf/gFzelEWc99orQ4X2cDdx7W34/VSOA+XxcS1At3cXebbvLceZ6Qnh/IC1rQC7eiF6ZpmrfLu44nnBu64fO6YMA4n4AuQV1UgAaeVm055gkhylDWYI2TJk3CZrMxd+5cb5D++OOP+fnnn5kyZQonT57EbrfTsGFDWrduzbZt2+jatSvffPONd0RgIYSoa2p90K53IoLtlx3H+K+LgN8OkRvTqqZLqnVUQAAqIMAbuN1hoZgRF0fgPh+FW6qthhWbUbHPWTiom8rTom66zgb1gmBecH/6uQruN9f1/BZ0PPerF36toRFh+t/I9qJ6aaZZzE+QEEL4Km2Ax7Zt2/Lxxx/TpUsXRo4cCcBf/vIX7rrrLp599lni4+PRNI3p06djsVgYP348EydO5J///CctWrSgX79+NfzphBBlObfnpiifsoY6q9VB+5NPPiHl9595Ys1fgCME/5QoQbsKFQRuPTcPw34cZbWgrDbPV5sVDAvK0FG67hkVuY7+QhcO6uejcFB3uV2e1+cEdZfpIiwirJIrFxcTpeueR3zlPxtSCCFKUtbYOgcOHCh2vX/84x9FpjVv3pwPPvigcgsUQlSZwMBAsrKyCAkJkbBdAUopsrKyCAwsuVt/rQ7aBWJaduN08BHYsxvuvK2my6n1CgI3bjeay4XmcKBlmIBCU4XaaC0WTKsFZbGgbDaweII4uoGyGHJvaQnKE9RzXDnFtoaLOsQwPEFbCCGEEKIETZs25ciRI5w6daqmS7noBAYG0rRp0xLn1+qgPW/ePLLTT3NT72vZftkyOu/bW9Ml1S2GgTI890AXG/lME0wTPScPsnLQlBtNgdI0zxqa7m0V1xXoWVmeIG4UBHK9zraKC1Emw/CMS1HTdQghhBDCb1mtVp8eLKLy1OqgvXHjRpx52cTf1ovvrojg1o0pJGdnYwYH13RpAjxBWddR+T+FRQKBUuA20fLy0NMysKSlkD/EE/lR3BPELdZzuqcb+YHckCAu6i7DQMvN9fweye+BEEIIIUS1qtVBuzCtXUf0/3yNa99u9Kuvq+lyRHloGlgMFAYqKBDTcc69pkqBaXq6p+floZkFz89WgOaZX9A93Wr1BHGL9ex94oZ0Txd1gNvtGRNBCCGEEEJUmzpz9vWnq28Fvub0ji9pLEG7dtC0s93TrdYSu6drbjeaMwctMwvPfeKgNCjcPd202YreJ17QKi7ExUrTPI/4kqAthBBCCFGt6szZV8vLO3K4voFl376aLkVUJz2/9ZoS7hNXCtxun/vEi3ZPt6Js+aOnW62+QbyMYf2FqFH5F5rkp1QIIYQQonrV6qAdGBiIgRvwjNR8ouUlNP/5GCdc2YRY5D5tQX73dEu57hPXcnJ8uqdrCiwZdqxZ9pIfYybd00UNUoYBTmdNlyGEEEIIUefU6qC9dOlSUo7/Sl7yHwDo7WNp9v0x1iVtpkdMXA1XJy4Khe4Th6JBXAUHo3Jz5TFmwj/JI76EEEIIIWpErQ7aALphRZmeVu16sT3h7bWc3LEJJGiLylKJjzEr0j29IIjLqNHifOg6msMhXceFEEIIIapZrQ7aM2fOxJmbxT23XA9AXnQ0bg2C9h/AYTqw6bYarlDUCRV4jJmne7o7f+B0TR5jJi6MYaDl5MgjvoQQQgghqlmtDtrfffcdptvFvbd0B0AFBZLa7BI6Hv2DbSm76NHgmhquUAjK7J7u8xgzhwMtw10wg5IeY6bj8ow2jXRLr9M0zdOjQh7xJYQQQghRrWr9WbimaZia6X2v2rbnmuOw8eR3NViVEBVQ8BgzqxUVEIAZHJz/L8TzNSQE02oFU6Fn52BJTsN2/A80e1ZNVy78gNI0T9AWQgghhBDVpg4EbR1dnX0MU17rq6ifA4cSt+Ay5eRT1BK67hlkLSAAMzgIZbVQwh3joo7RAE2CthBCCCFEtar1QRtNw2rYcCkXADlXxQAQc9jOrnR5prYQopbTNHA4aroKIYQQQog6pVbftFevXj0AbLZAMt0OLLqV3ObNcAfYuO64i42nttClXvsarlIIIaqOPEtbiLrD6XSSmJhIZmYm4eHhtGrVCptNBn4VQoiaUCUt2qZpMmnSJIYOHcqIESM4fPiwz/zPPvuMgQMHMnjwYD788EOfecnJydxwww0kJSVdcB2LFi1i0aJFBASEoNz592lbLORGt6LXyWA2ndqCUtK9VghRMWUd4zZt2sTgwYMZOnQoK1asADwnwE899RTDhg1j+PDhlXKMKxfDQMvN9d4+I4Sonb766isGDRrEggULWLNmDfPmzePOO+9k48aNNV2aEELUSVXSor1x40YcDgfLly9n165dvPLKK8ybN887/9VXXyUhIYHg4GDi4uKIi4sjIiICp9PJpEmTCAwMrNR6dFsgetbZk8yc1jHErE7kTLad/Zm/0CY8ulL3J4So3Uo7xjmdTl5++WU+/vhjgoKCiI+P56abbmL37t24XC6WLVvGli1bmDVrFrNnz676Yi0WNLsd7cwZVIMG8pgvIWqp+fPn89FHHxEaGuqdlpmZyX333UefPn1qsDIhhKibqqRFe8eOHfTo0QOAjh07snfvXp/5MTExZGZm4nA4UEqh5Z/4zZgxg2HDhtGoUaNKqWP69OlMnz4dwxaImX+PNkD2VTFYHS7an9b48tSWStmXEKLuKO0Yl5SURNOmTYmIiMBms9G5c2d++OEHmjdvjtvtxjRN7HY7lmp83JYKDUVPT0c7fVpatoWopZxOZ5GGioCAAO85lhBCiOpVJWd6drvd54qqYRi4XC7viWWrVq0YPHgwQUFB9O3bl/DwcFatWkX9+vXp0aMHCxcuLNd+DMMgKiqq2HkWi4Uff/wRgEZNLiM78ziBEeFoaBjXdAYgPuMKZpz4gsfb3U+kLfxCPnKFGBaDyIiIattfeflrXeC/tflrXbm6G4thIbKE3w9xYUo7xtntdsLCwrzzQkJCsNvtBAcHc+zYMW699VZSU1OZP39+ufZV1nEuKioKR2YapsuJJSAYXdfRtWKuodarB3a7Z2C0Sy7xjFRfhQpq8zf+Whf4b21S18Vh6NChDBw4kM6dOxMWFobdbmfHjh2MGDGipksTQog6qUqCdmhoKFlZZ5/ha5qmN2QfOHCAr776ii+//JLg4GDGjRvH559/zieffIKmafz3v//lp59+Yvz48cybN4+GDRuWuB+3201ycnKx86KionDmDwCUkp5Orj2HLDMZi26FsFAahYcxKLkJzzgOM3nHP3mh9ZOV+B0oXWREBGnp6dW2v/Ly17rAf2vz17oc9gwi3K4Sfz8KNGnSpJoqql1KO8adOy8rK4uwsDDee+89unfvzlNPPcWJEycYOXIka9euJSAgoNR9lXWcS05O5vQfv5HryAKrZ9AjQzOwGTZsug2bxYaOjqEZaJqGkZqClpyMWcVhu6A2f+OvdUEFaiutV8L5zitlflRUFMlnzlTb/so7LyoqimS7HQyj1N3UlePc3XffTa9evfjxxx/JysoiNDSURx99lAYNGtR0aUIIUSdVSdCOjY1l8+bN9O/fn127dhEdffYe6LCwMAIDAwkICMAwDOrXr09GRgZLly71LjNixAimTJlSasiuCKXr2AwbduXGghU0jZyrYmjy6wn+Mmww7x5eyW2X9OLq+h0qZX9CCP9kt9v55ptvcBR63NWdd95Z4e2Udoxr2bIlhw8fJi0tjeDgYH744QceeOABkpKSsFqtAEREROByuXBX0vOtlVLYNBuGJRgA03TjdDvJdeeiHEUDi3b6GHrG77gvaYTFGoBFs2BoBhbdgqZpaGjer54V8L4+d553+XPeX1QDTSoFplnsV02ps+9dLs9XtxtME83l8t1GMbTSvg+m6fs+NRUjLa34eefWe77dgUtat6DO4ualp2OkpqLOZ5+l1KqVtL/Saik8325HCw1FBQdXvK5aateuXfzf//0fdrud8PBwcnNzueWWW6T7uBBC1IAqCdp9+/Zly5YtDBs2DKUU06dPZ+3atWRnZzN06FCGDh3K8OHDsVqtNG3alIEDB1ZFGWfpOgG6jQyV452U3TqGRos/4pEmr/CfU98x9cAbrOw6j0Cj9NYlIcTF65FHHqFRo0beFq7zPfks6xj3zDPP8MADD6CUYvDgwTRu3Jj77ruPCRMmMHz4cJxOJ08++STBVRQQdN1Ax8Ba0gJhwZCTg/uPUzgaNcRh0TGViVIKRQmBEQ2F8n4FvO+Lk6KlkJaWhq7p3iCu6WdDuYbm6eaeP1SIruk+ob24eQV1eIO/UmgKbxjWTIUG6EoDZXrmmSaaqcDt9iyXl4d+5szZ9wVhOT8QqvyvRT6VpnmCpqZ5egIUvD77TS/2+6Dy1y1R4XlBQajc3OLn1aTQUFQVPCLugi/FlNGSXde88MILmKZJz549CQkJISsri2+++YbvvvuOl156qdR1TdNkypQpJCYmYrPZmDZtGs2aNfPOT0hIYPHixRiGQXR0NFOmTMHtdjNhwgSOHTuGw+FgzJgx9O7dm3379jF69GiuuOIKAOLj4+nfv3/VfGil0PLy0O1ZaE4X7shwVFBQ1exLCCEqqEqCtq7rTJ061Wday5Ytva/j4+OJj48vcf0lS5ZUSh3e7mKGgaFbfE4Ic66MRjNN6iUdZdJVf+ev/3uGhQc/5PE/j6qUfQsh/I9Sitdff/2Ct1PWMa5X+Hy8igAAIABJREFUr1706tXLZ35ISAhvvPHGBe+70gQFYcnNxXI6BWfjRmCt3IuMobZQnFanT3gveK1QKNON6VIo5QnCChPcJkqZ+a25CpyeFmQtvwUZtxtdKXDlh2TTRCsIx2igefbkaVE388O5JxAr3fM6I68eGZkZaJqBputomic06+Tf254fsguHex0dXde9f0OKBH8oMTV6Lgic8/7cZfK35TCdOExnkWXKc0GorHVK26+oHX755Rc++OADn2m9e/dm2LBhZa5b2pMUcnNzmTVrFmvXriUoKIixY8eyefNm0tLSiIyM5LXXXiM1NZWBAwfSu3dv9u/fz6hRo7j//vur5HMWDtdGph1NKUyLBTQN24mTmIEBuOrXQ1XyE2yEEKKiqm/Y2xrw5ptvel6YJhbd4tOVMeeqGACC9ydybYe7uKNJX947vJJbGt9AdFiLmihXCFHFYmJi2L17N1dddZV3ms1mq8GKapYKDETLy8N24iTOSxqjbCW2gRdaSfl2rTYVkB96FfnTTXQFRkoKmtvMD8suz/IuN+SvoxVqQSY/IufvBNBQmu6ZpOmg6SjD5lnGphVtUS6nwKAwcvPc+R/l7EUAExPTbfq01hd+7S2r0PtzW/SLtPCromG2uHUKZBlZpGell9hLwLtOoad1FLvfcihuneKCt45Opp5JemZ6scsU2Uah2wi82zintb+4/RaeVtBr4dw6zt2mYVpxBlhq94lMBZimyQ8//ECXLl28077//nvvLSulKe1JCjabjWXLlhGU31LscrkICAjglltuoV+/ft7ljPweBnv37uXgwYN8+eWXNGvWjAkTJvgMHnm+NIcDI8eO1Z6N5nZjGgZmYKBPbxK3zYbmcGA79gdmkARuIUTNqht/n3Qdw7CgOfF2D3TVr4fjksYE7T8AwFOt/so3Z7Yz5adZLLl6JoYmXdKEqG22b9/Opk2bvO81TePLL7+swYpqngoIgDwH1hN/4KofCaby3Htsmp6QrDwtygXdrz3HUCgcijWloXyCkYb+/9k78/ioqvP/v+82S7aZLOyyRhZFkaICKrTKooJSxCAIViiCWMRadywigqCguP1EwLrRihuKG4JWRWlRalWsfC0qWFlEMEDIPslklnvv749ZMpM9ZJlJct688rr3nu0+dxJm5nPOc57HbyKXlIYFsSnJIINpVZs84nlNBPaYB+4fbwu6iVoiPtVfe8OmosJ+8tAkgKZoaLIWbFJ1m0gMjMgGGHr0fvOKfaoaoy5t/B6wJia3kS8ytbN8+XKWLVvGLbfcAgQmI0455RSWLFlSa9+aMinIshwOqLZu3TpKS0s577zzwpMuLpeLG2+8kZtuugmAAQMGcMUVV3DaaaexZs0aVq1axbx582q8f12yK5Qc+haLJCG1a1e39xCvF8lViqmbGOlpmPbGF9yqqpKamtro4zYUYVf9iVfbyvQy0tPTsaliwqgl0qo/nxYuXAgQcPHUNDSvgt/0o0qBLwyuswfhfH8L2tFjODu0Z17fOdy5azmv/PwOV3Wrf4AkgUAQ32zcuBHTNMnLy8PpdIZXYNo6ptUCPh/q8WDAKznkch0IJmlKCqYmgcVSZ3Vq2m2YXk/TGi5oXKpZrZaR43JyQjG9tTdqQ3Tr1i3s7l1fasqkELpesWIF+/fvZ+XKlWGRnZ2dzdy5c5k6dSrjxo0DCKdtDZ3XRejXJbtCcX4his2O5KtfEEkpLw/p6FFMmw1/mjMwudhIf8ipqank5+c3yliNibCr/sTcttB2KN0AQw8c/X40m0xuiYLNVrNXSFvJrtDSiN2yQjPw7bff8u233wJgqipWRUM3y9+gj027EkxovzYQ8XxMh/MZln42j+9dS3bZsZjYLBAImo7PP/+cUaNGMXPmzHBAM0EAU9MwEuwBgWy1YlotmBYLqCqoSnkAMIFA0OoYNGgQ27ZtA6iUSQECCxcej4fVq1eHXciPHz/ONddcw+23387EiRPDbWfOnMk333wDwGeffUb//v2b6SmqxrRYMBITQdfRfjmC5cBBtEOHUY/moOQXBAKpud1IXl/Aa0cgaGyCgTclrw+prAy5pBS5sAgl5zhq9hG0nw9hOXAQ68FDaId/Qcs+inrsGGpeAWpufmDLlaBF0qpXtKPQNGxouChfYfF16kjeZZeQ/vpGjk/JwtO9Gwv6/ZEJ/57N0u9X8sTAe0WwGIGgFfHYY4/x0ksv0aFDB44ePcoNN9zAeeedF2uzBAKBoMFcffXV+CpEhw/t53/llVdq7FtTJoXTTjuNDRs2cNZZZzF9+nQApk2bxueff05RURGrV69m9erVADz99NMsWrSIJUuWoGkaGRkZdVrRbg5MS3DyEAJZB7xepLIypIhUepJpBrx4NBVTs2BaNExNw1RkkBXM0KSjQBDCDGa1iFyJDv19+f3IPn9AZENEikQTUwr+LckSpqJiWqoOSGoiPHdaMm1HaKsqShW5XY9Nm0Lqpvfp8NRfOXjfQjrbO3BD5nRW/PAX/n70n4zpeH5s7BUIBI2Ooih06NABgA4dOmC1inR+AoGgdXDbbbexYMECVq1aVe9tMbVlUti9e3elPqNHj2bBggWVyvv371+rsI85ioIZfI0q7f43TdANpDIPUqkbydSRzJBIMgOBGS0ahqYhyTJyqRtTkTFlOeABJBZoWg9ViGjJ50Py+UEvF9HhgJ7BmJ4hEW0qMoamgfiu0WZpO0Jb01CqiMqqpzo5PmUiHZ5bh/273bhP7cfUruN578hWHvhhDeemD8KhpcTAYIFA0NgkJSWxbt06zj77bL788kscDkesTRIIBIJG4YwzzmD8+PHs2bOH0aNHx9qcloskgRpcvaYKIW4EMinIbg/K8TzUwsKgECcgzFQVQ9MCq+EWDRQVU1ECq+KKIoR4vBBMGSkFf5+SqqHkFyD5/Eg+b0BMG0alrBiBwJ5CRAvqRqsW2r16lafpMiUJVVKRTCkceTzE8SsvJ/2NjXR88jn2/78HUCSFe065iSu/uIGHf3iae/vfGgvzBQJBI7NixQpWr17No48+SmZmJvfff3+sTRIIBIJGY9asWbE2ofUjB4WWCmZCAkYFd/1AQCsdqcSHVBTaW2uGV8VNTcVUQ0LcEhTgarkQFzScCBEdcOX2BwS0Xy8X0aYRoZ8llNIyZJcr8LuVlUDQPLFNQNBAWrXQXrFiRflFcBZRlVV0U0eRIqJpJiRwbPpUOv+/NSR9+R9cg8+kb3Imv+9+Bc8eWM8lnUYyJG1gDJ5AIBA0BkeOHKFjx44cP36cSZMmhcvz8vLEqrZAIGhV/PTTT+zevZuTTjqJ/v3743K5OHToEP369Yu1aW0DOehGThWr4RAQgD4fkscTWE0NLv6E9vCK/eG1oOuByYxIEe31BYV04AfTAKRyTwMCGTTCItpmq+RZYCYmYvpjmF5R0Cpp1UI7CllGMk1sio0SfwlKhUfPGz+WjFffoOOTz/HjWb8CWea6nlfx4dFPuPf7x3h60AN0srUXwdEEghbI2rVr+fOf/8zChQuRpPJYDZIk8fzzz8fYOoFAIGg4pmmyePFicnNzOeecc/jmm29YuXIlixcvZsmSJaxcuZK0tLRYmymoy/5wT+37w02LBYJCvNXsD68YVMzQI0R0cG90cBm6XERLgckISapWRAsEsaJVC+3bb78dCK5sB1e0LYqFYl9xpbamxcLRmdPounQFjq3bKBx5PjbFyt2n/InZ/7mTi7dPI0VNom9yJv2SM+mb1It+yZn0TOyGJrfql1EgaPH8+c9/BmDdunXhsuzsbJF3UiAQtBpeeuklHA4HM2fOpGvXrgD83//9Hw8//DB33nknq1at4u67746xlYIaCe0Pp/b94ZKrlLBLeqhlcH+4rBvIpSXxsz88mCOaUKT3YI5oyecLuHMHRXR4N7RpBicXgiI6tBJt14SIFrQoWrVC3LdvX/lF0GVElUCqIigaQMHoC2j30mt0ePpvFP5mGKgqQ9IG8trQNXxdsIs9xfvYXbyXVw9twmMEwu1rkkZmUnf6JfWib3Jm8KcXyWpiczyiQCCoB88//zw2m42ioiLeeOMNhg8fHhbhAoFA0JJ59913efbZZ7n22mvZt28fZ5xxBgMGDGDXrl2cfvrprTImxXFPHv/O+5od+d+gyipdbB3pYu/IScGfFC051iY2LpH7w6uqD+4Pl4tdqPkFBFo18f7wyMjcplEuoiPTW+k6EqCmpKAVFQXtkoWIFrR6WrXQroipKKiYGKZRdQNF4ch1M+gx7x7SNv2dvMsuBaBPUk/6JPUMN/MbOgfdh9ldvJc9xXvZU7yPfx7/nLeyPwi36WLvSL+kgOgOrYJ3tLYTrucCQQzZvHkz69atY9asWWzevDmcE1YgEAhaOoqiYLPZaN++PX/+85+x2+089dRTDB06FABVbflf+dx6GV8X7Oaz3P/w77z/8INrPwApahKSJFFYwWMxWU0Mi+8u9g50sLZDlZXggouELElISEihY0RZXeqTXYmUlrqRkYL7rCP6RPSLqo8cr7b64HmoXq4wbmR9ZLnL8FMsuyrX+8uQvTqSy0Q2QTIDW6gUKbj322IBzQoWC5LFgqkoKIoWuP8J5Ig2FRlDVcORuc3ERAyxD1rQhmj577r1wNQ0FJ8PWZIrRR4PUXzuEEpO70/7tS+Sf/GowF6PCqiyQq/EbvRK7MbYjhcExjZNjnvz2F28jz3Fe/nBFVj9/jjnX2GXnpDr+Rnpp9DdchL9knvRK7Ebmqw17YMLBAIg8IUiJyeHjIyMwJeywsJYmyQQCASNghwMlHXo0CFOPfVUAJYtW8a1114LEI5N0RL58siXLP12ETuLd+Mz/WiSxq+c/bnp5GsYmjaIfsmZyJJMsb+Ew+4jUT+H3EfYX/oz23N3UGZ4Yv0oLZIE2UaiYidRsZOg2ElSE0lQ7SSqCSSoCSSpCSQqCYEyJYFENYFENdheTSBJCV4bwttT0LaoUWjruo4SdCUpKSnBarW27BlRVUXyequMPB5Gkjjyh2vInHsrGa+9Rc7VV9ZpaEmSaGdNp501neEZZ4fLS/Uy/ufaH1z53svu4n28sP9NyvTAm70qqZyc2D1q5btPUi9StKRGeWSBQFDOkCFD+N3vfsfDDz/M/fffz4UXXhhrkwQCgaBRsNlsHDt2jHPOOYfbbruNadOmUVpaSm5uLm63u0V/f/sh/wdceilTThrHORlnMch5Gnal8kJIsppIv+B3qYqYpkmx34VuGhiYYJoEzzBNEzPivC71JiZJiYkUuYqD9cF/EfWmGeyDiWEGzoyIstrry8ete72J3W6jxF0arjcwCMyzBOojbY2sr1humiY+00+p7qbEX0qp7sblL6VELyXbk0NJaaC8RC/Fa/gqveZVYZUtJCgBkZ4YPgaEeEDEJ5AQEuYRoj1BCbRPCor7RMWOVbYIT1FBXFPtu+4PP/zA3Llz2bBhAw6Hg88++4zly5fz5JNPcvLJJzenjSdM//79ows0DQwDq2LF7XdXijweovSM0yg6dwjtXnyVvPFj0VNSTtiGBMXGGY5TOMNxSrgsOSWJb7K/C69+73bt5ZPcL3k7+8Nwm862DoGga8mZ4f3fIuq5QNAwbr75Zm6++WYATj/9dDRNeJMIBILWwYwZM1i6dCmPPvoomzZtYuXKlTgcDlasWMGzzz7LpZdeGmsTT5irTrmKkf6eKDY7knJiEwaSJDX6nm2nw0GBFH+eUU6Hg4Jm9tjyGT5K/G5K9FJKdDel/lJcQXEeKtdVg9yS/CjRXqqXkuvN56D7l7Bod+tldbqnIslhMR4S70nhlfWKIj608m4PC/WAiA+cp5itbD+/IC6o9t3qvvvu45FHHgnnmB01ahRpaWksXbqUv/71r81lX4O49957owtUFckwsGgWXD5XjX2PXDeD3r+fQ7sXXuXI9bMa1S5FUuiZ2I2eid0Y0/H8cPlxT15g37crKMCL97I157Ow63mymkS/5F70TQoKcOF6LhDUiXvvvZeFCxcyefLkSpNVr7zySoysEggEgsZjyJAh/PLLL8yaNYvJkydz5513UlRUxLp16ygpKeGGG26ItYmCVowmazgtGk6qX5yq6wSAbuqU6mVhQV7iLw2K8Mor66V+d7i8RC+l2F/CUU9OlMg3qCY2UwQSUlCs2yNW3MtX1hNUe4SIr7DiHnKRD50rCajyCQaXE7QqqhXahmFw+umnR5UNGjQIn69uriHxiBncv6TJWrWRx0N4MntScOEI0je8zfGJ4/G3b9fk9mVY0xhmTWNYLa7nGw6/G95nJFzPBYLauf766wF45JFHME0TSZLwer1YLJYYWyYQCASNx4QJExg+fDjvvfcen3/+OXa7ndGjR3PeeefF2jSBoM4okkKymtgoGXxM08RteCiNEOph0R4U6i5/KUZotV13U+IvCa7KuznsLgyLdpe/FL9Zt2BuVtlS7h5f0QW+kki3B/exV16ZT1ctWBv8KghiRY1Cuyr8LShaYGj29oknnggUBIW2KqvlOQdr4OjMaTg++icd1r7I4Xk3NZmdNVGV67lu6hws/SUgvoNB17bnflXJ9bxvcPW7XzDlWGdbB+F6LmiTZGRkALB9+3Z+/PFH5s+fzzXXXMNvf/tbunTpEmPrBAKBoPHIyMhg7NixeDwi8JdAIEkSCYqNBMVGRg3t6rra7jW8QVf4oHu8XkqJ301p8BhaWY90nw+J9hxPHiX+Q2HBX5fgfF3tHdk2YWs9nlgQT1QrtH/961/zwAMPcP3115OcnExJSQlPPPFEOE1ETRiGwaJFi9izZw8Wi4WlS5fSvXv3cP3GjRtZu3YtsiyTlZXF1KlT8fl8zJ8/n8OHD+P1epkzZw4jR45s0MNlZ2dHFygKJoGZsrrg69yRvMsuJf3Njbh+NYCi84dhxsEKWMD1vCs9E7tyMeeHy4978iLczgPHf+T8O8L1PDHodt4rvP87U7ieC9oQL7/8cthV/C9/+Qu/+93vuOyyy2JslUAgEDQeixYtYtu2bbRv3z7swSO2yAgEjYNFtmCxWEjF0eCx/IZOqV4u0l16acTKe0CMd7KeeJwoQeypVmjPnj2bp59+mgkTJlBWVobD4eCyyy5j5syZtQ66ZcsWvF4v69evZ+fOnSxfvpw1a9aE6x988EE2bdpEQkICl1xyCZdccglbtmzB6XSyYsUK8vPzmTBhQoOFdiVkOeAwLklosoZu+FHkmoNqHJs+heR/f0G3ex/A/+gqCkdfQP7YC3H37V1lerBYkmFNI8OaxnnpZ4XLSvUyfnQdCO/53uPax+uH34tyPc9M7Ebf5EwGtutPd7UzfZN7NXrAEIEgHpBlGWswn6emaSfs4VHbZOLHH3/MqlWrUFWVrKwsJk2aBATE/ccff4zP52PKlClcccUVDX8ogUAgiOCbb75hy5Yt4XRfAoEgPlFlhRQ5qcbtnha8zWiRoLGpVmVKksTs2bOZPXt2eEa0rnz11VcMHz4cgIEDB7Jr166o+r59+1JcXIyqquGxL774Yi666KJwm1BasUYlYszaIo+H0FOd/PDiMyR9tZPUdz8gddP7pL/xDmU9u5M/ZjQFF43En57W+LY2EgmKjQGOfgxw9AuXVeV6/q/cr9goXM8FrZyRI0cydepUBgwYwLfffsuIESNOaJyaJhN9Ph/Lli1jw4YN2O12pkyZwgUXXMC+ffv4+uuvefnll3G73Tz33HON+WgCgUAAQPfu3fF4PNjt9libIhAIBG2aGlXmc889x/r163G73WiaxtSpU+u0ou1yuUhKKp+dURQFv98fzuHYu3dvsrKywkE6UiLSZ7lcLm688UZuuqn2PdGKopCenl71g6lqOHVPVJuiIlBV1ASVnNIcErU6BloYPYLi0SNwFRWT8P5HJL69mU6rn6HjX9ZSdt5QSsZfgvv8YVAH13JFVXA6Gu5y0hDSnWn8itOiynJ9+ezK3cN3hf/ju8L/8W3BD/wz5/NwtMYULYlTHb05xdGb/s4+nOrsTe/knliVpnenj4fXrCri1a4yWUdVVJzV/P9oq1x//fVccMEF7N+/n8suu4x+/frV3qkKappM3Lt3L926dQtnbDjzzDPZsWMH3333HX369GHu3Lm4XC7uuOOOhj+QQCAQVCA7O5sLLrgg7GUjXMcFAoEgNlQrtP/617+yf/9+Xn/9dZKSknC5XNx///0888wzzJpVc7qrpKQkSkpKwteGYYRF9u7du/nHP/7BRx99REJCArfffjvvvfceY8aMITs7m7lz5zJ16lTGjRtXq/G6rpObm1tlXXp6OgMGDACIaiMXF4Nh4Jb8FJUU4dPqH9wt/8IL4MILsBz8mdR3PyT171vI2LYdf0oyBaMvIH/MhZT1Pbla1/JY5DesC+mOVM6wn8IZ9lOgY6DMHXY938duVyDy+cv7345wPVfoFXQ97xfc/903uRcOrXH3lMTraxavdnldRTh0f7X/P0J06tSpmSyKD44ePcqzzz5Lfn4+F110ER6PhzPOOKPe49Q0mehyuUhOLt96kZiYiMvlIj8/n19++YUnn3ySQ4cOMWfOHP7+97/X6iVS24Rieno6Xmcqfr8Hi7XhEVobi3idhIpXuyB+bYtXu7zFeaSmppKYKiYUI3n44YdjbYJAIBAIqEFov//++7z44ovhPT5JSUksXryY3/3ud7UK7UGDBrF161bGjh3Lzp076dOnT7guOTkZm82G1WpFURTS0tIoKiri+PHjXHPNNSxcuJBzzjmnUR5u/vz5lcpMTUMKrtDXkuGrVrzdunL0D9dw9NrpJO34mtR3PyTtnffIeH0jZb16BFzLLxwR167ltWFXbJzu6MfpFVzPfy7NZo8ruO+7eB//zv0P72RvCbdpb00nWU3EptiwyVZsirX8GHFul21YFUuwzIY9WG8N1weOZRYPXp8Xm2JFk058b62gbXP33XczY8YMVq9ezVlnncWdd97Jq6++Wu9xappMrFhXUlJCcnIyTqeTXr16YbFY6NWrF1arlby8vGpFdIjaJhRzc3MpKMjH9PtRbPGTFSJeJ6Hi1S6IX9vi1S6rqZOfn0+ZUfNWs7Y2oagoCvfffz979+6lR48e/PnPf461SQKBQNAmqVZoa5pWKZCGpmnhL5M1MXr0aLZv386VV16JaZrcf//9vPPOO5SWljJ58mQmT57M1KlT0TSNbt26MWHCBB588EGKiopYvXo1q1evBuDpp5/GZrM18BEroKpgGChSI7o7KwquIWfhGnIWcrEL50f/JPXdD+i06mk6PvksxUMHkz92NMXnDsHUWn6Eb0VS6JF4Ej0ST+KiDr8Jl+d68sN7vveVHKRUd1Ome/AYHgp9xRzVj1NmeMJlbt1T53yEkcjIQTFuwa7YokV8JTFvwyZbIsps2BRLeV1QzFsjxX+wnUUWgr614fF4OOecc1izZk1Y7J4INU0mZmZm8tNPP1FQUEBCQgI7duxg5syZWK1Wnn/+eWbMmMGxY8dwu904nc7GejSBQCAAYMGCBUyZMoWzzz6bL774grvuuou//e1vsTZLIBAI2hw1BkPLzc2NWm05fvx4naJYyrLMvffeG1WWmZkZPp8yZQpTpkyJql+wYAELFiyos+F1IbTy/swzz5QXWixIhgGShCqrdYo8Xh+M5CTyLruEvMsuwXrgIM73Aq7lKdv/jd+RQsHoC/BfMQE6d4y7qOUNJd2ayrnWMzk3/cw69/EZfjxB8V1meCnTy4LnASEeqpMsMnkl+UGRHmjnDrbzRLQv9peQ482LKivTPfhMX72fR0KqJOatEavsNsVKsjUJxZAjBL4t2Cck6kMC3xK1um+PWLW3yhZkSUSHbQ4sFguffPIJhmGwc+dOLCeYrq+2ycQ777yTmTNnYpomWVlZdOjQgQ4dOvDll18yceJETNNk4cKFTRP0USAQtGk8Hk84a8uoUaNYu3ZtjC0SCASCtkm1CnPOnDlce+21/OEPf6Bbt24cOnSINWvWcMsttzSnfQ0iPz+/cmHERIFVsVLmL6s18viJ4unRjaNzZnL02t+TtOM/pL73IWkb30Xe8DaOzJ4UjBlN/kUj0VPb7qqWJqtoskqSWvPe0oa6Luqmjkf34jY8UWI+6qh7cBtleHRvsKxqMV+meyjV3eR5C/C6D1Hic1Oml+ExvHiME0vDYIsQ3TbFFhbzVsVS7lIfLgucR4p5q2LBHiHsk3wSXRl4wq9Xa2XJkiU88MAD5Ofn89xzz7Fo0aITGqe2ycQRI0ZUGdFcBEATCARNja7r7Nmzh759+7Jnz546eWbVlrJw06ZN/O1vf0NRFPr06RN+76yqz08//cSdd96JJEn07t2be+65R6QaEwgEbZJqFebQoUN54IEHeOWVV9iwYQMdO3ZkyZIlnHrqqc1pX6NjynJ4JdmqWCn1lTb9TVUF19CzcQ09G7momE6ffYFtw9t0euIpOq55luJzziZ/zIUUnzu4VbiWxyOKpJCg2kmgcdOdVJwA0E0dj+ELi/mQi3ykmI8+Lwuu3Fe/ml/oLQ5PEATaecLB6Gri4+696NdxcKM+b0tn7dq1PProo7E2o0nxGzr/c+3nv0W7+W/hbnYV7cHlLw1P5FhkS9A7I3C0yFpUnTX4o8laoEzRKpfJFqzB81BZoG2gXJPE+5hAECsWLFjA/PnzOXbsGB06dGDJkiW19qkpZWFZWRmPPfYY77zzDna7nVtuuYWtW7ei63qVfZYtW8ZNN93EkCFDWLhwIR999BGjR49u6scWCASCuKPGpdzevXtz9913R5X985//5De/+U01PVoAsoxpBNJVqXIgj3dzYqQkUzLpcg5fNBLr/p9Ife9DnO9/RMqnIdfyEeSPHU1Zn5Ob1S5B46BICgmKQoLSyLEFKmCYRmAFXfeGxXpZxOq76XFzamrfYGI2QYi9e/dSVFQUlVKwpeMzfGzJ2c43Bd+zq2Qv3xX9LzwRk6o5ON0YgGA/AAAgAElEQVTRlzTNSZnhxWN4wn83hb4ijhk+PMGJntCPz/DhN/UG2xUQ5FpYvAfOtSgxH1WmWMIiPlRe3i5yLC1C1EcK/eh7qZIqYiwI2iSnnnoqr7/+er361JSy0GKx8Morr4Tzcvv9fqxWK5988kmVfb799lsGDw5M8v76179m+/btQmgLBII2SbVC+4033uCRRx7BZrPx+OOP07VrVxYsWMC+fftattBWFCRJwgRUSW1w5PGG4OnZnSPXz+LI7BkkffkfUt99n7S3N5Ox4S3cJ/cif+xoCkaPaNOu5YKqkSUZu2LDrtiAyqLR68oXIqMK9u7dy5AhQ0hNTQ27Mn766acxtqphvP7VWl7YspxvO6ucktKbrC5jON3RjwEp/ehi73hCfwd+Q8dr+vDq5eI7JMS9hheP4cNrePEGy72hOj107UPSJIrcxRHtfeXtDC8uvRSv14vXDPbRveG+HsOLScMmQUMxFiwVVt4TNDsKSmDlvYKYD5RFi/bQqr5Fil7dt8gWNFlFQUaWgj/IyJKELCnISOFyBRlJklAkGQk5cAyWB9oH+tp0K17Di4wSLhcI6sqNN97I448/zrBhwyrV1fY+V1PKQlmWycjIAGDdunWUlpZy3nnn8d5771XZxzTN8PtOYmIixcXFtdpelzSGJY5kNHsiciPG1Wko8Zr6TthVf+LVNm9RPmnpadiShBZoiVT7brV27Vo2b95MTk4Oy5cv59ixY4wcOZKHHnqoOe1rEFV92BDhOq7IcRKISFVwnXM2rnPORikqwrHln6S+9wGdH/8LnVY9Q9G5gykYcyHF55wtXMsFggawdevWWJvQ6Ez/RuKmpyW+XbkU84xfNcqYqqyg0jDPjIbEVTBNE7+pRwnzSmI9QphXFPzlkwPl7T26F5/pw5BNSjyleAwvRT5XeMKg0qSBUf8Aik2BHBbyUvg8INglFCkwcRwQ8nK0uA+3qSDuI8qjxo68rnBPq8WK4dervGfUeDVMNshUcR9JQkGpYhIiUF5V+/Jzmc6WZAZldK/9RWwjPP7448CJTR7WlLIwdL1ixQr279/PypUrkSSp2j6R+7FLSkrq5EFUlzSGxYXFKB4/khI/QjteU98Ju+pPvNpmwU9ebh5WT82eZm0tjWFLodp3K6fTicPhwOFwsHfvXhYtWtTiVrJvvvnmyoWSBIoChoEsy1hkC37dh6rEh4DVU1LIu3wceZePw7rvAKl/34Lz/Y9wfPIZfqeDggtHkD9mNGW9M2sfTCAQRPGf//yHxYsXk5ubS/v27bnvvvs45ZRTYm1Wg/BOnIT/0cfo9ehT7H3mCVDjZAKxAUiShCYFAiUmktCoY9f1y5RpmvjMoFjXKwr4gPD3GV5008DEDBxNAx0DwzQxTB0DE8M0Aj/h8tB54FpHxzQD/a02CyXu0sB1uE1E+8jxwtfR94myh+C1aaJHtou4f6iPz/CXjxdhp24aSGUSPt0ffj4z6jkCY1d8DULPFLahgR4KVSEj80WXf2Ano9HHbsl8+eWXuN1uTNNkyZIl/OlPf2LcuHE19qkpZSHAwoULsVgsrF69Oiykq+tz6qmn8vnnnzNkyBC2bdvG0KFDm+ZBBQKBIM6pMb1XiM6dO7c4kV0TpqKAroMsk2RJIt+Tj0p8CO1IPL16hF3Lk7/8itTNH5D25iYyXn0Td+9M8seMpmD0BcK1XCCoI0uXLuXhhx/m5JNP5ocffmDhwoW88sorsTarQZgOB4dvvI4ei5aT8dqbHJ8yMdYmtQokSQq4i8sWqCUrQmMRrysqjWGXaZpVTzZUI+6jhHuEYI+cVMhQraRaxedfRVasWMFDDz3E4sWLefnll7nppptqFdo1pSw87bTT2LBhA2eddRbTp08HYNq0aVX2AZg3bx533303jzzyCL169eKiiy5q8mcWCASCeKRaoV1QUMD27dsxDAOXyxXlilSlS3YcctVVVwHw4osvRpWbmobkCQQLsirWZg+IVm9UheJzBlN8zmCUwiIcW/5B6nsf0vnxJ+m06mmKzh1C/tjRFJ8zGNT4cakSCOKN5ORkTj45EGiwT58+2GxNG7SuObjqqqvwugrYfM7ZdHj2eQp/Mwxf546xNksgiCLkDq40YugIq3li6RRbO1arlfT0dFRVpV27dni9tb9OtaUs3L17d5X9KvYB6NmzJy+88EI9rRYIBILWR7WqrH///mzatAkIuAFt3rw5XNdShHZZWVnVFaoKpYG0XpqsBYKjmQZSCwg8oztSyMv6LXlZvw24lgejljs++VfAtfyikQHX8pN7xdpUgSDuSE9P56677mLo0KF8++23GIbB+vXrAZg8eXKMrTsxysrK8Hp9HLr5evrNvJEuDz3OgYfvC8eiEAgEbYvExERmzJjB1KlTefHFF8XeTYFAIIgR1QrtZcuWNacdzYumIZmBHWOSJJGkJlHiL8HaxCmZGhtPrx4cmXstR66bQfLnO0h970PSXt9Ixvo3cPc5udy13Bl/URQFgljQq1dgAuqnn34iKSmJwYMHk5OTE2OrGgdf+wyO/mEGnR9djfODjym4aGSsTRIIBDHg8ccf5+DBg+EtMldccUWsTRIIBII2Sdv0M1ZViHAXt2t2ir3F0FJjCKkqxecNpfi8oSiFRTi3bMX57od0/n9r6LjqaYrPHUL+2AspHnqWcC0XtGluuOGGWJvQpORedinOD7bSaeVfKB5ylphkEwjaEK+99hpXXHFFOCp4JLfcckuMrBIIBIK2S5tUXWaFDyCLbMGU4nyfdh3RHSnkZo0nN2s81r37SX33A5wffIxj23Z8qc5A1PKxF+LJ7BlrUwUCQWOjKBy640/0vmYunZ54ikMLbo+1RQKBoJno2DEQmyHkuSMQCASC2FLjpuS9e/eGz3/++Wd++OGHJjeoMRk1ahSjRo2qXKEoUYlGFFnBKlvxx0ne1MbCk9mTI3+8jt1vvsiB5YspHdCf9Nc30mf6Hzj5mrmkb3gbpSD+ItwKBIK6M2rUKH59ztnha09mT3KumkTq37eQ9OVXMbRMIBA0J8OHDwcCwciKi4uZMGECn376aaVUXQKBQCBoHqoV2u+//z7XX389xcXFAOTk5PDHP/6RLVu2NJtxDWXOnDnMmTOncoUsI1WINJ5kScLXyoR2GFWleNhQDt63kN1vvcQvN10PQOfHVtPvsql0W7CE5O3/Br8/xoYKBE2Ly+Xi0UcfZf78+XzwwQf89NNPsTapwcyZM4fpV14eVXZs+lQ8XU+i84qVSNUFhRQIBK2SpUuXcu655wJw0003hdNuCQQCgaB5qVZoP/fcc6xfv57k5GQABg0axEsvvcRTTz3VbMY1GYpSKSKvVbFi0jrcx2tCdzrInTieH59bxf/WriYv67ck7vwvPebdQ+dRv6XjE09h3Xcg1mYKBE3C/Pnz6dq1KwcOHCAjI4O77ror1iY1CabVwuE7/oT1l2w6PCfS7AgEbQlVVcNpDLt27Yosx39GFYFAIGiNVLtH22Kx4HQ6o8rS09OxWq1NblRjkZWVBcDrr78eXSFJAbFtGBD8ANJkDVmSW0yar8agrHcm2b0zyZ4zk+R/f0n7D7eS8dpbtHvldUr79iZ/7IUUjj4fPSUl1qYKBI1CQUEBEydOZOPGjQwaNAjTbPmTa1lZWXhLi3nm4SVR5SW/GkDeuIvJWP86hb8Zhrt/vxhZKBAImpPOnTvzyCOPMHDgQL755hvat28fa5MEAoGgTVKtopQkqVIearfbjc/XOtyrzZDQDiJJEglqAl7DG0OrYoSqUjzsHHIfe4Dv33qJX278A5Jh0OXRVfQbP5VuC5aS/K/Pwa/H2lKBoMGEYk8cOXKk1a/0ZM+Zhd/p5OTr/kTPG24j9Z33kF0lsTZLIBA0IcuWLSMtLY1t27aRnp7eutO1CgQCQRxT7Yr2tGnTuPbaa5k+fTpdu3blyJEjPPPMM/zud79rTvuaDFPTkLzRojpRS6TE17a/hOqpTnInTSB30gRs/9tL6nsfBqKW/+MTfOlpgajlY0bj6dUj1qYKBPXmrrvuYv78+ezdu5cbb7yRe+65J9YmNSlGSjI/PvsEqZvfx/n+R5z0wGN0fnQ1RcOGUnDRSIqHiJR/AkFrQ1VVEhMTSUtLo0+fPrhcLtLS0mJtlkAgELQ5qv2GNWrUKNLS0njttdc4duwYXbp04dZbb2XgwIHNaV/ToapQYcVek7UYGROfhFzLj8yZSdK/vyT13Q/IePVN2r28gdJT+pA/5kIKR/1GuJYLWgx9+/Zl/fr1sTajWfFnpJMzfSo506Zg3/0Dzr9vwfnRP3F+vA2/00HBqPMpuGgk7n59KsWuEAgELY+FCxfSvn17/vWvf3Haaacxb948nn766VibJRAIBG2OGpcyBg0axKBBg+o9qGEYLFq0iD179mCxWFi6dCndu3cP12/cuJG1a9ciyzJZWVlMnTq11j6NjsWCZBhR4c9UWUWTNfyGD1WI7jCmplE8/FyKh5+Lkl+A88OPSd38AV0eeYJOK/8SWB0beyHFZ58JqhJrcwWCahk+fDh5eXmkpqZSUFCAxWIhIyODe+65h/POOy/W5jUtkoT7lL64T+lL9h+vI/nfO3C+v4W0je+SseFtPJ07oTsdmLIEsowpK6DImLIcuFZkkILXUeVKsL0MshQ8KljsNux+f3m7yHHC10rEtRS4rrJdxf5KBTukgL1ytH2BMimqXHU40EpKo54hVF/eV4l45kB/MQkhaCkcPHiQ++67jx07djBixIjWEcRWIBAIWiDVCu1hw4aFzyVJQtd1MjMzWbJkCT169Khx0C1btuD1elm/fj07d+5k+fLlrFmzJlz/4IMPsmnTJhISErjkkku45JJL+Pzzz2vscyKMGzeu+kpFgSoCISVZkigoKxBCuxoCruWXkzvp8oBr+bsf4PxwK86tQdfyi0YGXMt7NuEkiUBwgpx99tnccMMN9OrVi4MHD/LEE08wd+5cbr/99hYrtMeNG0fxsYP16xRM+Vc8bChysQvHPz4h+V9fIHs8SLoeiF9hmkheH7JhgKEj6QYYBpIROIbOqyuXDZMEXUcyAuOF28VBALpOJ9DHrGKyAEnGlCgX44ApSYHrkDAPngfaBQW7BCAF2wbbICGrCu1Ms4q+0dfhsoixKt+HiL4R9wmOZUZOHkSNWT6eKQf6ahaNFL8/bKcpRdsSZU89nzl8nyrsNGt75rQUXLP+cAK/zdaNruvk5eUhSRIul6vVx6IQCASCeKVaof3pp59WKtuxYweLFy9m7dq1NQ761VdfMXz4cAAGDhzIrl27our79u1LcXExqqpimiaSJNXa50T4/e9/X22dWc0KhVW2YmBU0UNQkbLemWT/aQ5Hrp9F8mdfkvru+2Ssf512L72Gu8/JeLp0Qnc68TtT0B0O/I4UdGcKfocD3ZGC3+nAbEFR7AUtnyNHjtCrVy8AunXrRnZ2Nt27d0dR6ueJUZsHzscff8yqVatQVZWsrCwmTZoUrsvNzeXyyy/nueeeIzMzs8HP9Pvf/55jP3yF6fefUH8jOYn8cWPIHzemwbZE4nQ4KCgsrFxhmlHCvFyo62CYEeV6eTvdQDKDxwgxH7iu0M/Qo9vpBpjl90m0WiktKQle60hGpD2VJxQq22mArgcmDEITEqYJJmAGrjEJTiiEys3gxK4ZuF9osiHcN9DOoql4vd5AG8rLw32j7kNE3/LxoscMto+8b+jajLSl/D5ShbEwTRRJRtb9VdwndB5hTx2euar71P7MZoW+JrrTSclV0xvhr7V1cfPNNzNlyhRycnKYPHlyq01jKBAIBPFOvaLgnHXWWXWKOu5yuUhKSgpfK4qC3+9HDQbd6d27N1lZWdjtdkaPHk1KSkqtfapCURTS09OrrFNVFbvdDkBCQkLlBh4PlJZCxD0BTNPEXeDGrtmRaBpXQUVVcDocTTJ2Q2iQXePGUDhuDMXHc0l49wPsW7eReOAgcsF/kQsKq13FMux2jFQHhtOJnurEcDowUkPnznAdGWmkJSdjOB1xFbwpXn+XZbKOqqg4q/n/0VZp164dDz30EL/61a/4+uuvycjIYPv27Wha/TxYavLa8fl8LFu2jA0bNmC325kyZQoXXHAB7dq1w+fzsXDhQmw2W6M9U2lpKe6yMmxx9P+iRoLpFc3g5EZzr29L1U0AxAHVTk7EmHi1y2p6UZOTY21G3JGdnc37778f3iYjiW0PAoFAEBPq/c3M5XLV2iYpKYmSkvLo3YZhhAXz7t27+cc//sFHH31EQkICt99+O++9916NfapD13Vyc3OrrEtPT2fs2LFAFXm0Afx+lIICzComDvxuPznFOViUplltjdcvLY1il6aSN34sjB9bXqbrKMUu1MJClIIi1MIilMJC1ILQsRClsAg1Lx91/0+BdiWl1d5CT0rC73RUWCkPHh3BcqcDf7DOSEoMu3U2NvH6u/S6inDo/mr/f4To1OlEnGhbLg8++CDr169n27Zt9OnThz/+8Y989913PPLII/UapyYPnL1799KtWzccwQmYM888kx07djBmzBgeeOABrrzyykbdM3n11VdXmUdbIBC0TV599VV++9vfikjjAoFAEGPq7Dru9Xr54IMPOPPMM2sddNCgQWzdupWxY8eyc+dO+vTpE65LTk7GZrNhtVpRFIW0tDSKiopq7NMkyHKVe7QBErQEXD4XFoRbc6OgKOhOB7rTAXXcui35fAHxHRThKV4fnuwjAREeKi8oRDuWg+1/P6IWFCJ7q/a2MBUZf0pK2F29kiivINb9TiemzSqCH7VCFEXh9NNP55RTTsE0TT788EMuvfTSeo9TkweOy+UiOWKVLTExEZfLxRtvvEFaWhrDhw+vl9CuzXNH0zQMVSXZmYLFmljvZ2kq4tXbI17tgvi1LV7t8hYHVmwTU4XnTiRer5fLLruMnj17hvdnP/zwwzG2SiAQCNoe1QrtzZs3R13bbDb69OmD2+2uddDRo0ezfft2rrzySkzT5P777+edd96htLSUyZMnM3nyZKZOnYqmaXTr1o0JEyagqmqlPk2KLJcHRKsgqCyypWnvLagVU9PwZ6Tjzwh8gdJqWzk2TaQyT/mqeUFB5ZXzwiKUgiKsBw6SUFiEWlQU2HdZBYbFEi3CK6yUh0S71qUzqqqgpyRjWsTfTbxzww034PP5OHbsGLqu0759+xMS2jV54FSsKykpITk5mXXr1iFJEp999hnff/898+bNY82aNbRr167Ge9XmuePz+fD7/RQXFKHYTmyfdlMQr94e8WoXxK9t8WqX1dTJz8+nzKg5xkJb89y57bbbYm2CQCAQCKhBaC9btix8/s033/DCCy/w7LPPctFFF9U6qCzL3HvvvVFlkUF/pkyZwpQpUyr1q9inqTFVFXS90p5fVVaxyBZ0w48it5B9j20dScK02/DZbfg6dqhbH8NAdpWgFhZFCPTCCFFeGBbrliPHAuXFlbdOdAwe9YSEwIp5aOXckVKFW7ujPCBcclJgskfQbLhcLl544QXuuusu7r77bmbMmHFC49TkgZOZmclPP/1EQUEBCQkJ7Nixg5kzZ3LxxReH21x99dUsWrSoVpEtEAgEdSUnJ4fnnnuOhIQEZs6cWXV8GoFAIBA0G9WqSK/Xy+bNm3nppZfQNA2Xy8VHH33UqEF8Yo2pqkjVROoNpfkSQrsVI8sYKcl4U5Lxdu1Stz5+P2pRcViEp/h8eH45Er3XvKAQNS8f2/6fUAoLUdxlVQ5lShJ6SnLVIjxKrAfLnQ6MhATh0t4AQtHF3W43NputTsEdq6I2r50777yTmTNnYpomWVlZdOhQx8kfgUAgOEHmzZvH6NGjKSwsZMWKFdxzzz2xNkkgEAjaNNWqyBEjRnDppZeyYsUKevTowaxZs1qcyI5MqVMlmhaIPl5FxGGrbMVs9ni4grhHVfGnpeJPS8VDHVzaAcnjKRfhVQWEC7q5W37JRv1uN0phEXI1E0CGqlZeMQ+7tTvDKdRKkmzQb2ATvAAtmwsvvJBVq1bRr18/Jk2aFLXPuj7U5rUzYsQIRowYUW3/devWndB9q2LSpEkUHTnQaOMJBIKWid/vD3sL1pTeVCAQCATNQ7VCe9q0aWzatInDhw8zceJEzGoCh8UzkydPrrmBqlYbEM2iWALpvarYwy0Q1AfTasXXoT2+Du3r2MFELi0Nu7JHBYCr4OZu27s/INqLiiulUMt7txf6wMFN8EQtl8zMTIYMGYIkSfzmN7+Jyn3dUpk8eXKD8mgLBILWQWQaL8OoOv5IdRiGwaJFi9izZw8Wi4WlS5dWen90u93MmDGD++67j8zMTN544w3efPNNADweD99//z3bt2/n559/5g9/+AM9evQAAtsFQ1lgBAKBoC1RrdCePXs2s2fP5osvvuC1115j165drFixgvHjxzd9RPBGIhRAqLqIvWgakmFUuW4tSRJ21Y5H9zRZmi+BoEokCSMxESMxEV+XOgbxqZBCze8qILVfy/h/2pysXLmSoUOHAtC3b98YW9M45Obmkl9QiDMpfiKOCwSC5sftdnPgwAEMw6CsrIwDBw6EF0l69uxZY98tW7bg9XpZv349O3fuZPny5axZsyZc/9///pd77rmHo0ePhssuv/xyLr/8cgAWL15MVlYWKSkpfPfdd8yYMYNrrrmmCZ5SIBAIWg61bkAePHgwgwcPpqioiLfffps77riDt956qzlsazCzZ88GqsmjTWCPbE0kWhIpLa0+n7NAEDdUSKHmdeWT2kS5w1sykiQxd+7cqLQ3t9xyS4ytahizZ88WebQFAgFWq5W777670rkkSTz//PM19v3qq68YPnw4AAMHDmTXrl1R9V6vl1WrVnHHHXdU6vvf//6XH3/8MbwnfNeuXezfv5+PPvqI7t27M3/+/BPepiMQCAQtmTpH+kpJSeHqq6/m6quvbkp7mhdFqXEXtkW2iH3aAkErIisrK9YmCAQCQZPQkNgPLpcrSgwrioLf7w+nLTzzzDOr7fuXv/yFuXPnhq8HDBjAFVdcwWmnncaaNWtYtWoV8+bNq/H+iqJU632oqirp6emUOJLR7InIFYLUKj8fIuH9jzA1DV/vTHy9MzEy0ptl21+85pgXdtWfeLXNW5RPWnoatiRnrE0RnABtO6S2LCOZ1UtpVVbRZE2k+RIIWgnjxo3jzTffJDs7myFDhtC7d+9Ym9T8VNjLX/EdUEIScSkEgjZGUlISJSUl4WvDMMIiuyaKiorYt29feEsOBLIypKSkhM+XLKnd20bX9fB2v4qkp6eTm5tLcWExisePpKjIpW5S/vEJqe9+QNLO/1bq409JpqxXDzw9e1CW2YOynj0o69UDI7lxV9bjNce8sKv+xKttFvzk5eZh9eg1tuvUqY5bDQXNSttWj4pS6xfKJC2JQm8hSht/qQSC1sA999xD+/bt+de//sVpp53GvHnzePrpp2NtVtNhmrh1NyZmQEAHiQyaJBO9xcDAwDTNSn2qIlRf1XSlxadRprvD7SL/BQvLSyRJCHyBIIYMGjSIrVu3MnbsWHbu3FnnWDxffvkl5557blTZzJkzufvuuxkwYACfffYZ/fv3bxwjTZPE/9tF6t+34Nj6CYq7DM9JnTky+/fM/PTfeGWZv8yejm3fT1j3HcC2/wDODz5CKSnfAuhLdWLarBiahqlpmJbgUdMwLJbguRpRVl5vWrRAv3CZBXtKMobfHzVOuF3F8SOuEVu7BII2QdtWj7Ic+KkhsrhVsbbIiOsCgaAyBw8e5L777mPHjh2MGDGCp556KtYmNRlluhvTNHFanSRpSSiyUq/+ofc9EzMsvEPlobJwPdFtDdMgNSEV2StjGEZYvBumEdXGxAzUGWajCfyAYC8/ryjwfYYP3fALgS8QRDB69Gi2b9/OlVdeiWma3H///bzzzjuUlpbWmMFl//79nHTSSVFlixYtYsmSJWiaRkZGRp1WtGvDsm0bp978J6zZR9HtdgpHnk/+2AspPf1UkCSOffEfAEoGDaRkUERqS9NEO5qDbf8BrPsOYD30C5LXi+TzIfl8yF4fkt+P5PWhlpSWlwXrJa8XOXSuVx3JvZpwuzViKkrVIl6LLqso0MPiX1WDZZboCYHg0e5woPt9Ee00DM1S9YRAc4t/0wTDANNECh4xTCRDDxxNAwwzUK/r9WoXWR5qF+5vGOV9wu0MJMME08Bus2O6XMF2FWw0jOixTQP0is9gBNtG2mBUetZKZYYZbZsZtCk4nv+kDrjmL2qe342g0WnVQnvatGm1tjFsNiSfDyyWKustSrBcpPkSCFo8uq6Tl5eHJEm4XK5wQLSWzLRp0yjK3h++9us+PKaHJC2JVGsq6gluewmtekcK1/qQbE3Gb6l/yrGGCvzQdXUCX5bkJhH4VbWLKKhTW49uw6t7qh0n6rrSLaSq2xHtwVB5HPG51loYNmwYAD6fD7fbTadOnThy5Ajp6el8/PHHNfaVZZl77703qiwzM7NSu4r7wGfNmlWpTf/+/XnllVfqa36NKEePUnZSZ47+/iqKRvwG026Lqp804ZKqO0oSvo7t8XVsT/E5DUx3qethUS77fEheHw6bleK8/GBZUMCHhbq/XKR7fRVEvLd8nGDbULvIPnJJKWpF8R85SaBX706cUc/HMxU5QthbygW5qgTFrAFmHYRkFe0ihWTXNrR4ZUoSyBKmHPSglSVMSQZFDtbJIEmYwYU/U5ZAksvbyTI+dymuWD+I4IRp1UJ7/PjxtTdKTEQ6fhyzGqEtSzJ2NQGf7kVTqm4jEAhaBjfffDNTpkwhJyeHyZMnc9ddd8XapAYzfvx4jv3wFbrfi9dXiqZodLJ3wqbaau8chzRU4NdGanIqdr+9UvmJCvzIvpH9I68jicxvHDkWgF2141W9DR7HIKLONInsGllX0fbq0Hwqbn90Bo6aJiZqm7SIrKvYNvI5ahtHllr5lzEri4MAACAASURBVJh68umnnwJw2223ceutt9KpUyeOHj3KsmXLYmxZw3FfcQVH+nVBsdmRlMq/9YtHnt/0RigKpqJgWq3h/0V+hwOPM4YBtKoQ/5LPh8NqpTg/P6qsKiFfvqof3S5K2Ot6uSiU5YAolKQIUVhBIIbaVSEkrXY7ZV5v9e0UuYLQLG8XHkeqoizUrra+coX6oOhFkkhOSaGopKRcGEe0C9imRNlQbbtgGVLjeEtZ8CKSDLdcWvVn1OHDhwHo0qVLtW1Mi6XWLxpJlkSOlBSjmzqKpKDKKpLU8lfCBIK2RnJyMu+//z55eXmkpqZWWulriRw+fJi8nOOkOZNIt7cjUUtsFc/V3DS1wK8LqYmpqN7YfSxX/CwMid5UZyr5Zn6Nwr8u49RWV+u4FcZJ1TRKxAR4JQ4dOhQOjNShQweys7NjbFHTc+ToMQA6dmgfY0uamSrEP4DP4aAsDgN7xWvAMQCbw4EnTm0TtFxatdC+8cYbgerzaANgsSBBDR/tYFfsdErshM/wUeYvo0wvwzCN8Ey7IilosibEt0AQ5zz22GMUFBRw+eWXc8kll5CYmBhrkxrMjTfeiFlWxuurV6FYWuYqtiA+qM7FXJZkZEmO2QREdSRqGh5ZFUk4K5CZmcntt9/OgAED2LlzZ42puVoL85esAOC5J1bE2BKBQCAop1UL7Tohy5hWK/h8oGlVNpEkCbtqx46dFEsgZYXf8OM3/GHx7dE96KYeXg2RkVElFbmeAYgEAkHT8eSTT5KTk8Pbb7/NzJkzyczM5L777ou1WQ1GkqR6BzsTCAStk9tvv52dO3fyv//9j7FjxzJy5MhYmyQQCARtEiG0ATMpCSk/v1qhXRWqrKLKKjZsJFuSAdANPSy+PbqHMr0Mj788sI0sBcS3QCCIHX6/H6/Xi2EYKIoQpwKBoHUxZ84cXn75Zc4///xYmyIQCARtGqH6ANNqRTZriyNbO4qsoMgKVqwkkQSAYRqB1W/TT5kv4HZe4isJB5aRJAlVUgMCXOyrFAialOnTp+PxeJg4cSKPP/44GzdujLVJAoFA0Kg4HA7+9re/0bNnz3BmhVBEcoFAIBA0H0JoQyC1l2HU3u4EkCUZi2LBgoUENQEAh9NBjp6D3/Tj8XvK930HQ1nIkoxKYMVciG+BoPGYP38+Ho+HF154gccee4wLL7ww1iYJBAJBo5Kamsru3bvZvXt3uEwIbYFAIGh+WrXQvu666+rWUFEC6b10HZrBlbQq8W2aZnjlO0p8B4OuhVa+FVkRQdcEgnri9XrZvHkzL774IhaLBZfLxZYtW7DZWn7wsOuuuw45NzfWZggEgjihYjqvY8eOxciS5mPalVmxNkEgEAgq0aqFdn1Wq8yEBKTiYrBXzq/aHEiShKZoaGjY1YANpmmimzo+wxcV8dw0zaiI5yLdmEBQMyNGjODSSy/loYceokePHsyaNatViGwIvM/Jhw83mVeOQCBoWTz++OO89NJL+Hw+ysrK6NGjB5s3b461WU3K+cOGxtoEgUAgqESTCG3DMFi0aBF79uzBYrGwdOlSunfvDkBOTg633HJLuO3333/PrbfeysSJE7nzzjs5fPgwsiyzZMkSMjMzG2THjz/+CMDJJ59ce+OEBKSCgrhKExLevy2rIuK5QNAApk2bxqZNmzh8+DATJ06slMu3JfPjjz8iHz3Kyd26xdoUgUAQB2zbto1t27Zx//33M2PGDBYvXhxrk5qc/Qd/BqBnt64xtkQgEAjKaRKhvWXLFrxeL+vXr2fnzp0sX76cNWvWANCuXTvWrVsHwNdff82jjz7KpEmT2Lp1K36/n1deeYXt27fz2GOPsXLlygbZMW/ePKCWPNpBzHpEHI81NUU89xreQMRzfxl6FRHPFblVOzEIBFUye/ZsZs+ezRdffMFrr73Grl27WLFiBePHj6dPnz6xNq9BzJs3Dzwe3gy+xwoEgraN0+nEYrFQUlJC9+7dcbvdsTapyVny4OOAyKMtEAjiiyZRXV999RXDhw8HYODAgezatatSG9M0WbJkCQ899BCKotCzZ090XccwDFwuF6razIJQ0wL7sw0D5Jbnhh0Z8TyZaPEd3vetl1HqLwGI2vetyi1nkkEgaAiDBw9m8ODBFBUV8fbbb3PHHXfw1ltv1Xucmrx2AD7++GNWrVqFqqpkZWUxadIkfD4f8+fP5/Dhw3i9XubMmSPy2woEgkanY8eObNiwAbvdzsMPP4zL5Yq1SQKBQNAmaRI163K5SEpKCl8rioLf748Szx9//DG9e/emV69eACQkJHD48GHGjBlDfn4+Tz75ZK33URSF9PT0KutUVUULrlJX16YSPh+43WC11q39CaKqKqmpqU16j+oIpRsL7/n2BVzPMcFjerAmWsIr5qF94PGAoio4HY5Ym1GJeLWrTNZRFRVnXf/22xgpKSlcffXVXH311SfUvyavHZ/Px7Jly8JfdKdMmcIFF1zAtm3bcDqdrFixgvz8fCZMmCCEtkAgaHTuvfdesrOzufjii3nzzTd59NFHY22SQCAQtEmaRGgnJSVRUlISvjYMo9IK9caNG5k2bVr4+q9//SvDhg3j1ltvJTs7m+nTp/POO+9grUH06rpObjXRdtPT0/H5fADVtqmI5PEg5+RgJibWqf2JkpqaSn5+fpPeoy7IyCSQgN204zf8JNmSOHr8KMX+YryGl1BmcUmSYp5uzOlwUFBYGJN710S82uV1FeHQ/bX+7Xfq1KmZLGpd1OS1s3fvXrp164YjOAFz5plnsmPHDi6++GIuuuiicDulGTIcCASCtsX69evJysqiS5cu7NixA1VV6xanRiAQCASNTpP4SA8aNIht27YBsHPnzir3QH777bcMGjQofJ2SkkJycsDl2eFw4Pf70XW9KcyrFlPT4ioYWnPx/9u79/im6jxv4J9zcuklLbQNoChQpVJ05MFSRGQVELDqso4K5dJ2FwbFYQfZUUGEwkCp3BlBVxhA0PVCWaVd9Hm47A7DchnRvlgWGBiEEVBUFEFa2nJJ2uZ2fs8fpzlN2rRpS9Kk6eftixfJueWbNn7J9/xu7hnPYw2xSIhOwK1xt6J7fHfcHnc7usR2QUJUAvQ6PaqValQ5K1HtrEK1swoOlx1CcKZjan8a6rXj3ufOZQBgMplgsVhgMpkQFxcHi8WCF198ES+//HKrx01EkWvNmjUoLi7WGhluvfVWFBcXY+3atSGOjIiofQpKi3ZGRgaKi4uRlZUFIQSWLl2KHTt2oLKyEuPHj0d5eTlMJhMkj9bRSZMmYe7cucjJyYHD4cD06dMRGxt7U3G89NJLzTvBaIQkSeqMxCFquQ0XTZnxvMpZDVvNWt9uOknHGc8p4jXWa6fuPqvVqhXely5dwrRp05CTk4Nf/vKXTXotf0Nk5s+fD1y5gsSEBMBobOlbCrhQDpFpTLjGBYRvbGEbV3U1kpKSgCD3QmsrDhw4gKKiIu27Vbdu3fDmm28iKysL06ZNC3F0wTXlV9mhDoGIqJ6gFNqyLGPhwoVe2zyX6kpKSsK2bdu89ptMJrz11lsBjWPIkCHNO0GSoMTEQHI61cnRqB5fM577Wm7M6ayGBAkCwudY74bGf7u7q3ueJyBgcOpR5aysdz3tcQP3RXwe28jj2ss1cKzkvV1AALwx0+6kp6dj//79GDlyZL1eOykpKTh//jyuXr2K2NhYHDlyBJMnT8aVK1fw3HPPIS8vD4MGDWrya/kbIpOWlgb5p59QcfVqWOWtcBkiU1e4xgWEb2xhG5fBgPLycojq6kaPay9DZGJjY73+jQIAg8EAUzu4EfHggHT/BxERtbKIXuvJPW6yT58+TT/JZIJ05UqbWu4r1BpabkxAeK1XLDw65je43UfnfQGBBFMCohxRXud5tqR7nqcotdsVKO4DtGOadV6dx0IRXtujnFGoVqq1uOreIPD7WIh6X4zqaui8xuaqUxRHo9ekm+Ov105ubi4mT54MIQQyMzNxyy23YPHixbh+/TrWrVuHdevWAQDeeecdREdH31QsJ0+ehFxSgj69egXirRFRGxUdHY0ff/wR3bvXriX9448/+v03JhKc/vocAODuXil+jiQiaj0RXWgvWLAAQNPW0XYTRqPaQkk3RRfgruMmgwl2vT2g1wyExIREVIjalp7m3ljw5OuY5tyQ8NwuSXEwGUy43qR3Qc3lr9fO8OHDMXz4cK/98+bNw7x58wIey4IFC7iONhFh5syZeOGFFzBo0CB0794dFy9exBdffIEVK1aEOrSg+/1b6ko1XEebiMJJRBfaLRJGYxyp7anbtdzjSevSqxPcQeFkdURE7UGvXr3w0UcfYe/evSgpKcG9996LadOmeU3cSO2QEFoPQ/d/7l5+nv+px9a/ka8NA5Qk6KCDTtJxHh6iJmKhXZcsq63aDkdYjXckIiIiakx8fDyeeeaZUIdBTRGAAljdVb+Hm8GhzmsDqA0AMmRIkgQJ6mNZJ6uPJVnbL8ve29zXlyBBgQK70w67YofdZYfLadP2CQjIkqxNhss5a4hqsdD2QZhMkK5dY6FNREREEU9RFOTn5+PMmTMwGo1YvHgxkpOTvY6pqqrCs88+iyVLlmhDZZ555hltVYVu3bph2bJlOH/+PHJzcyFJEnr16oUFCxZAloOymmxwNKMArnQYtILWrbEC2Ou4ABTAEiRtm7tHnQQJSQlJuCquBnR8fqy+diUgRShwKS64hAtOxakW4E67NmeN+73LkLUiXCexFZzaHxbaPojoaMgVFe1yTW0iIiJqX/bs2QO73Y7CwkIcP34cy5cvx3qPeR++/PJLLFiwAJcvX9a22Wxqq2ZBQYHXtZYtW4aXX34ZAwcORF5eHvbu3YuMjIwgvwO1uLO5qn22AgsIyJC1x41pTgGcFJsEg8PQ5AK47rZgcccZzOvLOhkG1G+QchfgLuGCQ3HA7lJbwatd1bA6rKh2VrErOrUbEV1o5+bmtuxEjtMmojYiNzcXcmlpqMMgojbs6NGjGDx4MAAgLS1NW7XFzW63Y+3atZg1a5a27fTp06iqqsJzzz0Hp9OJGTNmIC0tDadOncIDDzwAQF1mtbi4OKiFts1VjSnPZyNaFw2TwdRgK3AwCuD4qHg4Dc5gvK02SyfroINaOMcgRtsuhEDHjh1R6iyFCy6vrujuJWHZFZ0iTUQX2gMGDGjZiXo9hF4PuFyAjnfZiCh8DRgwAPJPP3HiOyJqMYvF4jVpmk6ng9PphF6vfk3s379/vXOio6MxefJkjB07Ft9//z1+/etfY9euXV7LVppMJty4ccPv6+t0OpjNZp/79Ho9zGYzrB3jYYgxQZbVmOyKHQ6XAx0NXZDzxD3qBKCtTK/XIzExsdVf159wjuvWzrfW2+7VFd3lhE2xwe6yw+a0aUuyunsl6GSd+kfSeU86e5N0eh0SOnYM2PUCxX69AknmJETHJYQ6FGqBiC60Dx8+DKBlBbcwmSBZLCy0iSisHT58GHJpKQb8n/8T6lCIqI2Ki4uD1WrVniuKohXZDbnzzjuRnJwMSZJw5513IiEhAaWlpV7jsa1WKzp06OD39V0uF8rKynzuM5vNKCsrw41rN6CzOSEkCTaXDQadAUnRSYhCFPYfKQYADLjvvqa83YBJTExERUWF/wNbWVuPS4aM6Jr/6nZFr3ZWw6E44FScahd0j1nR9dCr3dpb0BU9oWNHXL12rSVvK6iMcKK8rBxRNlejx3Xt2rWVIqLmiOhCe/ny5QCat462JiYG0rVrHKdNRGFt+fLlXEebiG5Keno69u/fj5EjR+L48eNITU31e87WrVtx9uxZ5Ofn4/Lly7BYLOjcuTN+8Ytf4NChQxg4cCAOHDiABx98MGBxVrmqoNMZYI4xw2QwaS3nS//wBwDA/33nnYC9FoWHel3Ra0Z3CiHUAlxx+eyKDqgFO7uiUyhFdKF9M4TRyCKbiIiIIl5GRgaKi4uRlZUFIQSWLl2KHTt2oLKyEuPHj/d5zpgxYzBnzhxkZ2dDkiQsXboUer0es2fPxvz58/HGG2+gZ8+eePzxxwMSo0FvgCkqDnFRHaDj5FntniRJ0Et66GuGEviaFd0pnHAprnqzogO1XdHdRbi/SfKIWoKFdkP0ekiyDKEoQFtaloKIqDkUBXA6ITkc6nNJgpAkNe/Jsnr33/2YiCKSLMtYuHCh1zb3El6ePGcYNxqNWLVqVb1j7rzzTmzevDngMXaJ7QJhMDAXkV/NmRXd5rTBoThQ5ahClbOyXld0nayDJPEzRy3DQrshkgQlNhZSdTUQFRXqaIiIAsPpBBwOSK6a8V46HZSYGIiEBHVOCpdLPabmb8ldhDtrZ9YVgDoFTU3LAGQZom5hLtg6QERE4aWhrugJCQm4olzx3RVdqP/+SUICJLArOjUZC+3GxMZCslggWGgTURsmVVaqMwELAREVBdGhA0RMjNo6pG/8i4JWLguh/lEUtQhXFEju506nVpxL7iK9qgpSzeRK7mtIkqQV4KJua7n7Ob+0EBFRK5MkCXrZf1d0p+JU1wev0xUdgLaknE7SQSezxKIIL7Rfe+21mzpfGI1e/wMREYWb1157DXJJScMHOBwQUVFQzGbAaGx5t0t3ESzLanEO+BzRpm0zm+G6ckUtxD3+1CvOHY6GW809CnN2aSeihiyaOTPUIVAEa0lX9Gpnlc9Z0dkVvX2J6EK7T58+N3cBoxGSJKnFNltZiCgM9enTp9F1tCW7HUrnzkB0dCtHBjVv6nReyyQ2WpwD9QpzrTj316VdqFPZSB7d1r1azdmlnShi9endO9QhUDvVolnR2RW93YjoQvvAgQMAgCFDhrTsApIEER2tfpEz1L+LRUQUagcOHIB85QqGDBjgc7+A2junzfDRSt1oce5uIW+s1bwpXdoVRc35dbuys9WcKOwdOHQIADBk4MAQR0Kkauqs6E7FCYfLAbvLd1d0g46Fd1sW0YX2W2+9BeAmCm0AwmSCXF6ujmUkIgozb731FmCz+S60hVAnLWtLhXZzNbfVvKEu7e6x502ZCM6zS7tnHIDavb2hOD3/9vXYbgfcs7/7O7ax6xK1M2+++y4AFtrUNjSnK3qcLMHJ5ezarIgutAOBE6ERUZvldELExLBFtq5Adml3t557Hlf3sfs49+OGjq07ht7HtaTGzm9MA0OghJ/9kiQBBoPaC8C931/3+7rXCsRNCF/n6PkVhogiS92u6IkGA8o4sVqbFZTfnKIoyM/Px5kzZ2A0GrF48WIkJycDAEpLSzFjxgzt2K+++gqvvPIKsrOzsWHDBuzbtw8OhwPZ2dkYO3ZsMMJrHqORY/qIqE3SxmfTzWtil/abYjZDCcTNXV9Fvb/Hje03m+GKjfW536sEbsrNhLqPPbf5Os/jJoVU9/zYWLWrPxERURgKSqG9Z88e2O12FBYW4vjx41i+fDnWr18PAOjcuTMKCgoAAMeOHcObb76JcePG4dChQzh27Bg+/vhjVFVV4b333gtGaM2n06njG51O3j0nojanTY3PpsAIdHfy6GggJsbnrta8DV3vtcxmoKysFSMgIiJquqBUjkePHsXgwYMBAGlpaTh58mS9Y4QQWLRoEVauXAmdTocvvvgCqampmDZtGiwWC2bNmhWM0FpEmEyQrl1joU1EbYe7dZCFNhEREVGrC0rlaLFYEBcXpz3X6XRwOp3QexSq+/btQ69evdCzZ08AQEVFBS5evIi3334bFy5cwNSpU7Fr1y51fFgDdDodzGazz316vR4bNmwAgAaPabLY2Nrua3q9OgP5TRTder0eiYmJNxdTEIRrXED4xhaucSEqCnq9Hub4+FBHEpEaGx4DqPlt7dq10Ov1yMzMxLhx4/ye01IrVqyAfPly/R1Op7pqArvWElGEe/13vwt1CERE9QSl0I6Li4O1ZvkUQP1Sqq9TmG7fvh0TJ07UnickJKBnz54wGo3o2bMnoqKiUF5e3miR7HK5UNZAtzGz2YxOnToBQIPHNEvHjpDsdnVpmOvX1ZloUbNOazML78TERFRUVNx8TAEWrnEB4RtbuMaFqiokms1+P/tdu3ZtpYAiS2PDYxwOB5YtW4atW7ciJiYG2dnZGDZsGI4dO9bgOTfjrrvughwTU28yLMnhgHKzNxmJiNqAu+64I9QhEBHVE5SmjvT0dG0N6+PHjyM1NbXeMadOnUJ6err2vH///vj8888hhMDly5dRVVWFhISEm4pj9+7d2L17901dQ6PXQ8TGQpjNUHr0gCs5Ga6uXSE6dFCX0LFYIFVWAlVVXkvBEFHkaWx4zLlz59CjRw907NgRRqMR/fv3x5EjR5o0pKYldu/ejd01+daLEFw1gYjahd2ffYbdn30W6jCIiLwEpUU7IyMDxcXFyMrKghACS5cuxY4dO1BZWYnx48ejvLwcJpPJq1v4sGHDcPjwYYwZMwZCCOTl5UGnu7l149xdxx977LGbuo5Per1X8Q2XC7DZIFVXQ7Ja1aVQUNPi7e5uTkQRobHhMRaLBfEeXfZNJhMsFkuThtS0xIYNGwCbDY89/HDtRo7PJqJ2ZP3mzQCAx4YODXEkRES1glJoy7KMhQsXem1LSUnRHiclJWHbtm31zgunCdCaTadTlxqJjYVISlILb7tdLbwtFq3whiQBHl+2iajtaWx4TN19VqsV8fHxTRpS44u/uSgMBgOgKEhMSKgtrO12ICEBCOHSXnq9/ubnxwiCcI0LCN/YGBcREVHzcRrtYNHpgJgYiJgYiMTE2sLbZgNkWS28a1r0hXuMd6CWYiGioEpPT8f+/fsxcuTIesNjUlJScP78eVy9ehWxsbE4cuQIJk+eDEmSGjynMf7monA4HIDDgYqrV7WeM1JlJZSkJIgQLn1kbsIcAaEQrnEB4RtbW4+Lc1EQEVEosNBuLR6FN8xmuKKj1a7mNpva1byyUj1Oklh4E4U5f8NjcnNzMXnyZAghkJmZiVtuucXnOUEjhDrjOBERERGFBAvtUJHl2hbvhAR1xuCaFm934S0ASACE0cjCmyiM+BseM3z4cAwfPtzvOUHF8dlEREREIRPRhfbq1atDHULTyTIQHQ0RHQ3RsWNt4W23azOaCyEgyTKEe3I1Ft5E7d7q1ash//xz7QaHQ51tnOtnE1E78YdFi0IdAhFRPRFdaN9+++2hDqHlPAvvmiXEtDHeVivkqioIRYHk7mrOwpuoXbr99tvVdRpr1tGWHA4oSUkhjYmIqDXdfuutoQ6BiKieiC603TObP/300yGOJAAkCYiKUluqOnSAy1142+1q4e1u8QbUFm+jkYU3UTuwbds2SOXleObRR7VtXD+biNqT//enPwEAnnn88RBHQkRUK6IL7U2bNgGIkEK7Ls/COz7eu/CurFQLb5dLbfF2dzVnV1KiiLNp0ybAZqsttIUAWGgTUTMoioL8/HycOXMGRqMRixcvRnJystcxVVVVePbZZ7FkyRKkpKTA4XBg7ty5+Omnn2C32zF16lSMGDECp06dwm9+8xvccccdAIDs7GyMHDkyqPF/uHUrABbaRBReIrrQbld8Fd4Oh3eLt6JAEqK2qzkLb6LIwvHZRNQCe/bsgd1uR2FhIY4fP47ly5dj/fr12v4vv/wSCxYswOXLl7Vt27dvR0JCAl5//XVUVFRg1KhRGDFiBP72t7/h2WefxXPPPReKt0L+CKEONVKU2sdCQKoZfgRJghBCfehxjqjpJaltkyQIWVb/vfH8Q0QaFtqRSpIAo1GdsTwurrbwdjjUwttqVdf2liQInY4zFBNFAI7PJqKWOHr0KAYPHgwASEtLw8mTJ7322+12rF27FrNmzdK2PfHEE3jcowVZp9MBAE6ePInvvvsOe/fuRXJyMubOnYu4uLhWeBftUN2CuaZBBQYDJKtVK5olIWqHE8qy+r1Pp1N7POp0gF6vPpZltaD2LJwlqfb6LlftazgcgNMJOJ2Q3N8vnU4AUFfNcZ8HqAW5JAF2u3qMTsfhjdQusNBuLzwLb5MJLkDtau5waF3NYbFAslrVBGwwqImQiNoUjs8mouayWCxexbBOp4PT6YRer35N7N+/f71zTCaTdu6LL76Il19+GQDQt29fjB07Fn369MH69euxdu1azJ49u9HX1+l0MJvNPvfp9Xp137VraqOAj1ZTQ02ciYmJTXi3gaPX6wPzmp6tzHUKZz8B1P6pKZih10MfFYWE22+vLWjrFs7BUqcg1x47nYDDAb0QSJRlteBu6L3Jcm3cOl3wY64RsN9lgOmrq9XPf3R0qEOhFmCh3Z7VLbw7dIBy6RJQVaW2eFdXq92F3GO8WXgThTeOzyaiFoiLi4PVatWeK4qiFdmNuXTpEqZNm4acnBz88pe/BABkZGSgQ4cO2uNFTVh6y+VyoayszOc+s9mMsrIy6K5eVYe++Si0HTUtqRUVFX5fK5ASExPrv2ZLuma7W5ndS7i6C+ea7171Wpkb66btdMLcsWODP8+QqbmZUlZWBphM9VvJ3T8zpxOw2Wpbyt1FO2payj0u6bPreguLcp+/yzCQaDCoPzM/hXbXrl1bKSJqjogutDdu3BjqENoWgwHCZFIL706daruaV1aqhXdVVe2YHKORhTdRGNi4caO6jrbTyfHZRNQi6enp2L9/P0aOHInjx48jNTXV7zlXrlzBc889h7y8PAwaNEjbPnnyZMyfPx99+/bFwYMHce+99wYzdADAu7//fXAu3FDX7JpCGQYDYLV6FX/N7prdSi22YcfdYl3zXVL4OETbJkRtK7nLpf4OFEVtGXe51PmI3H+7i3IhIMmyeq4k8edOIRHRhXZD3ZCoiQwG9e5xbKxaeDud2qzmktUKybPwNhjUO7BE1KrMZjPk6mpIViuUMOz2RkThLyMjA8XFxcjKyoIQAkuXLsWOHTtQWVmJ8ePH+zzn7bffxvXr17Fu3TqsW7cOAPDOO+8gPz8fixYtgsFgQKdOnZrUkTGhDQAAFehJREFUon2zzE3JfZ7Fmmcrs3v8sns8s+c5ej1EnVZmd/EsJAno3BlKfHxt0cbiLTgkyes7Zt2i3Ou5Z5d1942RmrHkcDggOZ1qUV5dXXNyzURvnuPa3TcBPLuvE7VARFdGhYWFANDgPxLUTO47srGxEDWFN+x2SFVVauFd0+2MhTdR6yksLIRUUYGcRx7h+GwiahFZlrFw4UKvbSkpKfWOKygo0B7PmzcP8+bNq3fMvffeiy1btgQ+yEZs2b4dAJD11FNq4ewuplyu2oMkCcJgUPOku8XZ/XdzumZ7io7mZLLhxv27q/kO6rcod998SUyEEh2tPq+Z2E2q6b7uOckbwJnXqekiuhIqKioCwEI7aGru8IrYWAizWU1ONhukmtY1yWO8l8Z951AIdUbKhrjvKNY8hs2mdhHy3Fb3D1E7VFRUBNhsyB4xgl/4iKj9EQKF27YBQqh5UJYhjEaImBiI6Gi1mHa3SPO7AnnyLIxjYtThkx68ivI6E7x5zbxe0229STOv152gjiJaRBfa1Mp0OrW1OzYWwr3EkHssk58/nms11pt9U1HU61ZVeU+Y4e4S5DmGypMkeSVJrbD3dVxDBbx7W0P7iMKEiI7mvAlE1H44nZBsNq+xvq477mAepODwGE8ONNJS7vm91HOitzrd1yWbzXuSN0lSn9dtJTcYWuPdUZCw0KbgamJR6msSDC9ms9ptvbFj6xbwQO3yEb4K+4aKes9JTzzHcnnMHuo5EYrWZd7rbTexqHf/XbewZ1FPzSS4Ti0RRTohgOpqSE4nhNEIpUsXiNjY2mKERTaFmuckbzWfS79Fua+Z192t47GxbPluw1hoU+RoRlHqt7D3d5y7gE5KguvKFf+t9Q0V9YCaYN3Huh/Xba1vJK56Rb17fBq1H5KktmgTEUUoAUCqqoISHw+lQwd1fDRvRFNb1oSZ12E2A+G2VBs1WVAKbUVRkJ+fjzNnzsBoNGLx4sVITk4GAJSWlmLGjBnasV999RVeeeUVZGdnAwDKysowevRovPfeez4n4iAKC+5/3GXZ7x30phb1jR7bnC747ucmE3DjRjNendosvd7vGptERG2Z0qULJ1olojYlKNlqz549sNvtKCwsxPHjx7F8+XKsX78eANC5c2dt1spjx47hzTffxLhx4wAADocDeXl5iA7QF0bP2TGJ2rSWdMHnxFjtgpbn2LJDRJEsJqbBXfy+R0ThKCid/o8ePYrBgwcDANLS0nDy5Ml6xwghsGjRIuTn50NX0yK4YsUKZGVloUuXLgGJIzY2FrGxsQG5FhFROGKeI6L2jnmQiMJRUApti8WCOI+JeXQ6HZx1xozu27cPvXr1Qs+ePQEAn376KZKSkrQCPRA++OADfPDBBwG7HhFRuGGeI6L2jnmQiMJRULqOx8XFweqxhrKiKNDXGVOzfft2TJw4UXv+ySefQJIkHDx4EF999RVmz56N9evXo3Pnzg2+jk6ng9ls9rlPr9dj165dAIBXXnnlZt5OwOn1+gbjDqVwjQsI39jCNS4gvGOjwNmxYwcAYNKkSaENhIgoRJgHiSgcBaXQTk9Px/79+zFy5EgcP34cqamp9Y45deoU0tPTtef//u//rj2eMGEC8vPzGy2yAcDlcqGsgZn4zGYzHA4HADR4TKiYzeawiwkI37iA8I0tXOMCmhZb165dWymayFJdXY1XX30VZWVlMJlMWLFiBZLca8fXKCoqwpYtW6DX6zF16lQMGzYMN27cwKuvvgqLxQKHw4Hc3Fz069cvRO+CiIiIiIIlKF3HMzIyYDQakZWVhWXLlmHOnDnYsWMHCgsLAQDl5eUwmUy1yxIREbUhH3/8MVJTU/HRRx/hmWeewbp167z2l5aWoqCgAFu2bMG//du/4Y033oDdbsf777+PBx98EJs3b8ayZcuwcOHCEL0DIiIiIgqmoLRoy7Jc7wuk51JdSUlJ2LZtW4Pnc/ZIIgpnR48exfPPPw8AGDJkSL1C+8SJE+jXrx+MRiOMRiN69OiB06dPY9KkSTDWzAbvcrkQFRXV6rETERERUfBxMUIiokb8x3/8Bz788EOvbWazGfHx8QAAk8mEG3XWK7dYLNp+9zEWiwUdOnQAoLZ4v/rqq5g7d67f1/c3F4XBYNBiCifhOkdAuMYFhG9sjIuIiKj5IrrQ/uSTT0IdAhG1cWPHjsXYsWO9tv3Lv/yLNuGj1WrVCmi3uhNCWq1WrfA+c+YMZsyYgVmzZuGBBx7w+/r+5qLYsmULAM5F0VThGhcQvrG19bg4F0Xk4/c9IgpHQRmjTUQUydLT0/HZZ58BAA4cOID+/ft77e/bty+OHj0Km82GGzdu4Ny5c0hNTcU333yDl156CatWrcLQoUNDEToRERERtYKIbtEmIgqG7OxszJ49G9nZ2TAYDFi1ahUA4P3330ePHj0wYsQITJgwATk5ORBCYPr06YiKisKqVatgt9uxZMkSAGrL9/r160P5VoiIiIgoCFhoExE1U0xMDFavXl1v+7PPPqs9HjduHMaNG+e1n0U1ERERUfvAruNEREREREREAcRCm4iIiIiIiCiAWGgTERERERERBRALbSIiIiIiIqIAYqFNREREREREFEAstImIiIiIiIgCSBJCiFAHQURERERERBQp2KJNREREREREFEAstImIiIiIiIgCiIU2ERERERERUQCx0CYiIiIiIiIKIBbaRERERERERAHEQpuIiIiIiIgogNp8oa0oCvLy8jB+/HhMmDAB58+f99q/b98+ZGZmYvz48SgqKgqbuHbu3ImxY8ciKysLeXl5UBQlbGJzmz9/PlauXBk2cZ04cQI5OTnIzs7Giy++CJvNFjaxbd++HaNGjUJmZiY++uijVovL7a9//SsmTJhQb3uoPv8UWMxzgY/NjXmuaXGFOscBzHORjnkusHG5tXaOa0pszHO+McdFINHG/elPfxKzZ88WQghx7Ngx8Zvf/EbbZ7fbxaOPPiquXr0qbDabGD16tCgpKQl5XFVVVWLEiBGisrJSCCHE9OnTxZ49e1olLn+xuX388cdi3Lhx4vXXXw+LuBRFEU899ZT4/vvvhRBCFBUViXPnzoVFbEII8dBDD4mKigphs9m0z1xr2bhxo3jyySfF2LFjvbaH8vNPgcU8F9jY3JjnmhaXEKHNcUIwz7UHzHOBi8stFDnOX2zMc74xx0WmNt+iffToUQwePBgAkJaWhpMnT2r7zp07hx49eqBjx44wGo3o378/jhw5EvK4jEYjtmzZgpiYGACA0+lEVFRUq8TlLzYAOHbsGP76179i/PjxrRaTv7i+++47JCQk4MMPP8Q//dM/4erVq+jZs2dYxAYAvXv3xo0bN2C32yGEgCRJrRZbjx49sGbNmnrbQ/n5p8BingtsbADzXHPiAkKb4wDmufaAeS5wcQGhy3H+YmOe8405LjK1+ULbYrEgLi5Oe67T6eB0OrV98fHx2j6TyQSLxRLyuGRZRqdOnQAABQUFqKysxEMPPdQqcfmLraSkBH/4wx+Ql5fXavE0Ja6KigocO3YMOTk5eP/99/E///M/OHjwYFjEBgC9evVCZmYm/uEf/gGPPPIIOnTo0GqxPf7449Dr9fW2h/LzT4HFPBfY2JjnmhcXENocBzDPtQfMc4GLK5Q5zl9szHO+McdFpjZfaMfFxcFqtWrPFUXRPqh191mtVq8Pa6jicj9fsWIFiouLsWbNmla9a9ZYbLt27UJFRQWmTJmCjRs3YufOnfj0009DHldCQgKSk5Nx1113wWAwYPDgwfXuRIYqttOnT+PPf/4z9u7di3379qG8vBx//OMfWy22hoTy80+BxTwX2NiY55oXV7jmOIB5LpIwzwUurlDmOH+xMc81D3Nc29bmC+309HQcOHAAAHD8+HGkpqZq+1JSUnD+/HlcvXoVdrsdR44cQb9+/UIeFwDk5eXBZrNh3bp1Wpej1tJYbBMnTsSnn36KgoICTJkyBU8++SRGjx4d8ri6d+8Oq9WqTVxx5MgR9OrVq1Xi8hdbfHw8oqOjERUVBZ1Oh6SkJFy/fr3VYmtIKD//FFjMc4GNjXmueXGFa44DmOciCfNc4OIKZY7zFxvzXPMwx7Vt9fsotDEZGRkoLi5GVlYWhBBYunQpduzYgcrKSowfPx65ubmYPHkyhBDIzMzELbfcEvK4+vTpg61bt+L+++/Hr371KwBqUszIyAh5bKEYy9PUuJYsWYJXXnkFQgj069cPjzzySNjENn78eOTk5MBgMKBHjx4YNWpUq8VWVzh8/imwmOcCGxvzXPPjCqccBzDPRSLmucDFFcoc15TYmOf8C4fPPt08SQghQh0EERERERERUaRo813HiYiIiIiIiMIJC20iIiIiIiKiAGKhTURERERERBRALLSJiIiIiIiIAoiFNhEREREREVEAsdBupq+//hpTpkzBhAkTkJmZidWrV0MIgUOHDmH69OmhDg8bN27EiRMnGtw/YcIEnDt3rt72zZs319v2448/4umnn8bs2bObHcd///d/4/Lly7hw4QLGjRvX7PNb6sCBAygsLGzSsaWlpcjPzwcAHD58GKdPn27Ra4bL754oUJjnmoZ5jqjtYp5rGuY5opZjod0M169fx4wZMzB37lwUFBSgqKgIZ8+exZYtW0IdmmbKlCno27dvs89bv359vW1/+ctfMGjQIKxYsaLZ19u0aRMsFkuzz7tZQ4YMafL6kZ07d9YS8yeffIKSkpIgRkbUNjDPNR3zHFHbxDzXdMxzRC2nD3UAbcnevXsxcOBA3HHHHQAAnU6HFStWwGAw4NixY9pxmzdvxu7du+F0OhEfH481a9bgp59+wpw5c6DX66HT6fD73/8eBoMBL7/8MoQQcDgceO2119C7d2/tOqNGjcK7776LDh06YODAgdi8eTN+8YtfYNSoUSgsLERhYSF27twJSZIwcuRITJw4Ebm5uRg5ciQeeOABzJo1CyUlJejatSsOHz6ML774AgCwdu1aXLlyBVVVVXjjjTewc+dOXLt2Dfn5+VqiunjxItavX4/q6mr06NEDaWlpWLRoEXQ6HaKiorBo0SIoioKpU6ciISEBQ4YMwa9//WsAwJ///Gd89dVXmD17Nl5//XWUl5fjhRdeQGlpKXr37o3Fixfj0qVLmD9/Pmw2m3a9rl27au/9008/xf79+1FdXY3S0lJMnDgRe/fuxddff41Zs2bh0Ucf9flz3rlzJ7799lvMnDkT7733Hv7zP/8Ter0e999/P1599VWsWbMGx44dQ2VlJZYsWYI5c+YgLy8Pn3/+OU6dOoXy8nLs27cPq1evBgBkZWVh9erV6NKlCwBAURQsXrwYJ06cgMPhwG9/+1vEx8cH/HdPFCrMc8xzzHMU6ZjnmOeY56g1sNBuhpKSEnTv3t1rm8lk8nquKAquXr2KDz74ALIsY/Lkyfjyyy9x+vRp3HvvvcjNzcWRI0dw7do1XLx4EfHx8Vi1ahW++eabencMR4wYgc8//xy33norunXrhuLiYhiNRtxxxx344Ycf8F//9V/46KOPIEkSJk2ahIcfflg7t7CwEN26dcPq1atx7tw5PPnkk9q+oUOH4umnn8aaNWuwa9cuTJ06FZs3b9aSMgDcdtttmDJlCr799lvk5ORg9OjRWLJkCe655x7s2bMHy5cvx6xZs1BaWopPPvkERqNRO/eRRx7BPffcg/z8fBgMBlgsFixbtgzx8fHIyMhAWVkZVqxYgQkTJmDo0KE4ePAgVq5ciVWrVnm9f6vVqiXXDz74AEVFRTh06BA2bdqE4cOH+/w5u505cwZ//OMfsWXLFuj1evz2t7/F/v37AQA9e/bEvHnzcOHCBQBAnz59MHjwYIwcORKDBw/G22+/jWvXrqG0tBSJiYlaUgbUf5wrKiqwdetWlJaWYvPmzfi7v/u7gP/uiUKFeY55jnmOIh3zHPMc8xy1BhbazXDbbbfhb3/7m9e2H3/8ET///LP2XJZlGAwGzJgxA7Gxsfj555/hdDoxZswYvPPOO3j++ecRHx+P6dOnY8iQIfj+++/xwgsvQK/XY+rUqV7Xfuyxx/D222+ja9eumD59OgoKCiCEwGOPPYazZ8/i4sWLmDRpEgDg2rVr+OGHH7Rzz507hyFDhgAAUlJSkJSUpO3r06cPAKBTp064cuVKk957SUkJ7rnnHgDAgAEDtCTarVs3r6TsS/fu3dGxY0cAgNlsRlVVFc6ePYsNGzbg3XffhRACBoOh3nnu14uPj0dKSgokSULHjh1hs9ka/Dm7ffvtt7jvvvu0695///34+uuvAQB33nlng7FKkoSnnnoKO3fuxIULFzBmzBiv/d999x3S0tIAqF2Vpk+fjkOHDgEI7O+eKFSY55jnmOco0jHPMc8xz1Fr4BjtZhg2bBg+//xzLQE6HA4sX74cZ8+e1Y45ffo09uzZg3/913/F/PnzoSgKhBDYu3cv+vfvjw8//BBPPPEE3n33XRw6dAhdunTBe++9h6lTp+KNN97wer3U1FRcuHABJ06cwNChQ1FZWYm9e/diyJAh6NmzJ+666y5s2rQJBQUFGD16NFJTU73OdXd/+uGHH1BRUdHoexNCNLq/S5cu2uQShw8f1rpbybLvj5AkSdo1JUmqt79nz56YOXMmCgoK8Nprr+Hxxx/3eY2GNPRz9rz+iRMn4HQ6IYTA4cOHtYTsK2bPeDMzM7Fr1y4cPnwYQ4cOrRe3+07rjRs3MHnyZL8xteR3TxQqzHPMc8xzFOmY55jnmOeoNbBFuxni4uKwfPlyzJs3D0IIWK1WDBs2DDk5Ofjf//1fAEBycjJiYmIwevRoGI1GdO7cGSUlJUhLS9PGlMiyjDlz5uC2227D9OnT8eGHH0KWZUybNq3eaw4YMAAXLlyALMsYMGAAvvnmG5hMJtx9990YNGgQsrOzYbfb0bdvX9xyyy3aeWPGjEFubi7+8R//EbfddhuioqIafW8pKSmYOXMmVq5c6XP/4sWLsWjRIgghoNPpsHTp0kav169fP8yaNQuLFi3yuX/27NnIz8+HzWZDdXU1fve73zV6vboa+jm79e7dG3//93+P7OxsKIqC/v3749FHH21wJsr77rsPK1euRLdu3ZCSkgKTyYS0tDTo9d7/i4wYMQIHDx5EdnY2XC6X1+8s0L97olBgnmOeY56jSMc8xzzHPEetQRL+bn1Rm/SXv/wFlZWVePjhh/H999/j+eefx549e0IdVtAVFRXh0qVLeOmll27qOv/8z/+MuXPnIjk5OUCREVGgMc8xzxFFOuY55jlqu9h1PEJ1794dGzZsQFZWFmbOnIm8vLxQhxR0n332GTZt2oSHHnqoxdeorq7G6NGjcffddzMpE4U55rmWYZ4jajuY51qGeY7CAVu0iYiIiIiIiAKILdpEREREREREAcRCm4iIiIiIiCiAWGgTERERERERBRALbSIiIiIiIqIAYqFNREREREREFEAstImIiIiIiIgC6P8DBSrR9A4gBksAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "performances_df_lr=performances_df_dictionary['Logistic Regression']\n",
    "summary_performances_lr=get_summary_performances(performances_df_lr, parameter_column_name=\"Parameters summary\")\n",
    "\n",
    "get_performances_plots(performances_df_lr, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Class weight for the majority class\",\n",
    "                       summary_performances=summary_performances_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.862+/-0.01</td>\n",
       "      <td>0.054+/-0.03</td>\n",
       "      <td>0.174+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.858+/-0.01</td>\n",
       "      <td>0.055+/-0.01</td>\n",
       "      <td>0.216+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.858+/-0.01</td>\n",
       "      <td>0.056+/-0.01</td>\n",
       "      <td>0.216+/-0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters          0.01              0.01               0.05\n",
       "Validation performance     0.862+/-0.01      0.054+/-0.03       0.174+/-0.03\n",
       "Test performance           0.858+/-0.01      0.055+/-0.01       0.216+/-0.03\n",
       "Optimal parameter(s)               0.01              0.05               0.05\n",
       "Optimal test performance   0.858+/-0.01      0.056+/-0.01       0.216+/-0.03"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to decision trees, lowering the class weight of the majority class provides a boost of performances in terms of AUC ROC. The impact on Average Precision and CP@100 is however mitigated: the only noticeable impact is an improvement of the Average Precision on validation data, which however comes at the cost of a higher variance (as is visible from the large confidence interval)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling\n",
    "\n",
    "We followed the methodology reported in Section 6.3 ([](Resampling_Strategies_Transaction_Data)), saving the results in the `performances_resampling_real_world_data.pkl` pickle file. The performances and execution times can be retrieved by loading the file. Performances were assessed for SMOTE, RUS, and a combined resampling with SMOTE and RUS. All the experiments relied on decision tree models, whose tree depth was set to 6 (providing the best performances as was reported in [Chapter 5](Model_Selection_RWD_Decision_Trees)). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "filehandler = open('images/performances_resampling_real_world_data.pkl', 'rb') \n",
    "(performances_df_dictionary, execution_times) = pickle.load(filehandler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE\n",
    "\n",
    "The results for SMOTE are reported below. The imbalance ratio was varied in the range 0.01 to 1, with the following set of possible values: $[0.01, 0.05, 0.1, 0.5, 1]$. We recall that the higher the imbalance ratio, the stronger the resampling. An imbalance ratio of 0.01 yields a distribution close to the original one (where the percentage of frauds is close to 0.25%). An imbalance ratio of 1 yields a distribution that contains as many positive instances as negative instances.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "performances_df_SMOTE=performances_df_dictionary['SMOTE']\n",
    "summary_performances_SMOTE=get_summary_performances(performances_df_SMOTE, parameter_column_name=\"Parameters summary\")\n",
    "\n",
    "get_performances_plots(performances_df_SMOTE, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Imbalance ratio\",\n",
    "                       summary_performances=summary_performances_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.879+/-0.01</td>\n",
       "      <td>0.051+/-0.01</td>\n",
       "      <td>0.15+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.882+/-0.02</td>\n",
       "      <td>0.056+/-0.01</td>\n",
       "      <td>0.168+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.883+/-0.02</td>\n",
       "      <td>0.056+/-0.01</td>\n",
       "      <td>0.168+/-0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters           0.5              0.01               0.01\n",
       "Validation performance     0.879+/-0.01      0.051+/-0.01        0.15+/-0.03\n",
       "Test performance           0.882+/-0.02      0.056+/-0.01       0.168+/-0.02\n",
       "Optimal parameter(s)                1.0              0.01               0.01\n",
       "Optimal test performance   0.883+/-0.02      0.056+/-0.01       0.168+/-0.02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show that the benefits of SMOTE are mitigated. Creating new synthetic instances of the positive class tends to increase AUC ROC performances (left plot). It however comes with a decrease of performances for both Average Precision and CP@100 metrics. These results are in line with those observed on simulated data (Section 6.3, [](Resampling_Strategies_Transaction_Data_Oversampling))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random undersampling\n",
    "\n",
    "The results for random undersampling (RUS) are reported below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "performances_df_RUS=performances_df_dictionary['RUS']\n",
    "summary_performances_RUS=get_summary_performances(performances_df_RUS, parameter_column_name=\"Parameters summary\")\n",
    "\n",
    "get_performances_plots(performances_df_RUS, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Imbalance ratio\",\n",
    "                       summary_performances=summary_performances_RUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.885+/-0.0</td>\n",
       "      <td>0.064+/-0.02</td>\n",
       "      <td>0.203+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.892+/-0.02</td>\n",
       "      <td>0.066+/-0.01</td>\n",
       "      <td>0.201+/-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.892+/-0.02</td>\n",
       "      <td>0.066+/-0.01</td>\n",
       "      <td>0.201+/-0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters           1.0              0.01               0.01\n",
       "Validation performance      0.885+/-0.0      0.064+/-0.02       0.203+/-0.02\n",
       "Test performance           0.892+/-0.02      0.066+/-0.01       0.201+/-0.04\n",
       "Optimal parameter(s)                1.0              0.01               0.01\n",
       "Optimal test performance   0.892+/-0.02      0.066+/-0.01       0.201+/-0.04"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_RUS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, RUS allows to improve performances in terms of AUC ROC, but comes with a noticeable decrease of performances in terms of AP and CP@100. These results are also in line with those observed on simulated data (Section 6.3, [](Resampling_Strategies_Transaction_Data_RUS))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining SMOTE with undersampling\n",
    "\n",
    "We finally report the results for combined resampling (SMOTE followed by RUS). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "performances_df_combined=performances_df_dictionary['Combined']\n",
    "summary_performances_combined=get_summary_performances(performances_df_combined, parameter_column_name=\"Parameters summary\")\n",
    "\n",
    "get_performances_plots(performances_df_combined, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Imbalance ratio\",\n",
    "                       summary_performances=summary_performances_combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.883+/-0.01</td>\n",
       "      <td>0.039+/-0.01</td>\n",
       "      <td>0.091+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.883+/-0.02</td>\n",
       "      <td>0.045+/-0.01</td>\n",
       "      <td>0.119+/-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.887+/-0.01</td>\n",
       "      <td>0.046+/-0.01</td>\n",
       "      <td>0.119+/-0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters           1.0               0.1                0.1\n",
       "Validation performance     0.883+/-0.01      0.039+/-0.01       0.091+/-0.02\n",
       "Test performance           0.883+/-0.02      0.045+/-0.01       0.119+/-0.04\n",
       "Optimal parameter(s)                0.5               0.5                0.1\n",
       "Optimal test performance   0.887+/-0.01      0.046+/-0.01       0.119+/-0.04"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we observe that resampling allows to improve performances in terms of AUC ROC, but decreases performances in terms of AP and CP@100. The results are in line with those observed on simulated data (Section 6.3, [](Resampling_Strategies_Transaction_Data_Combining))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensembling\n",
    "\n",
    "We followed the methodology reported in Section 6.4 ([](Ensembling_Strategies_Transaction_Data)), saving the results in a `performances_resampling_real_world_data.pkl` pickle file. The performances and execution times can be retrieved by loading the pickle file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "filehandler = open('images/performances_ensembles_real_world_data.pkl', 'rb') \n",
    "(performances_df_dictionary, execution_times) = pickle.load(filehandler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performances were assessed for balanced bagging, balanced random forest, and weighted XGBoost.\n",
    "\n",
    "### Baseline\n",
    "\n",
    "For the [baseline](Ensembling_Strategies_Transaction_Data_Baseline), the hyperparameters were chosen as follows:\n",
    "\n",
    "* Bagging and random forest: 100 trees, with a maximum depth of 10. These were shown to provide the best performances for random forests in [Chapter 5, Model Selection - Random forest](Model_Selection_RWD_RF).\n",
    "* XGBoost: 100 trees, with a maximum depth of 6, and a learning rate of 0.1. These were shown to provide the best trade-off in terms of performances in [Chapter 5, Model Selection - XGBoost](Model_Selection_RWD_XGBoost). \n",
    "\n",
    "The baseline performances are reported in the table below. It is worth noting that the performances for random forest and XGBoost are the same as those reported in [Chapter 5, Model Selection - Random forest](Model_Selection_RWD_RF) and [Chapter 5, Model Selection - XGBoost](Model_Selection_RWD_XGBoost), respectively.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "performances_df_baseline_bagging=performances_df_dictionary['Baseline Bagging']\n",
    "summary_performances_baseline_bagging=get_summary_performances(performances_df_baseline_bagging, parameter_column_name=\"Parameters summary\")\n",
    "\n",
    "performances_df_baseline_rf=performances_df_dictionary['Baseline RF']\n",
    "summary_performances_baseline_rf=get_summary_performances(performances_df_baseline_rf, parameter_column_name=\"Parameters summary\")\n",
    "\n",
    "performances_df_baseline_xgboost=performances_df_dictionary['Baseline XGBoost']\n",
    "summary_performances_baseline_xgboost=get_summary_performances(performances_df_baseline_xgboost, parameter_column_name=\"Parameters summary\")\n",
    "\n",
    "summary_test_performances = pd.concat([summary_performances_baseline_bagging.iloc[2,:],\n",
    "                                       summary_performances_baseline_rf.iloc[2,:],\n",
    "                                       summary_performances_baseline_xgboost.iloc[2,:],\n",
    "                                      ],axis=1)\n",
    "summary_test_performances.columns=['Baseline Bagging', 'Baseline RF', 'Baseline XGBoost']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Baseline Bagging</th>\n",
       "      <th>Baseline RF</th>\n",
       "      <th>Baseline XGBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUC ROC</th>\n",
       "      <td>0.912+/-0.02</td>\n",
       "      <td>0.912+/-0.02</td>\n",
       "      <td>0.928+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average precision</th>\n",
       "      <td>0.072+/-0.03</td>\n",
       "      <td>0.08+/-0.03</td>\n",
       "      <td>0.095+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Card Precision@100</th>\n",
       "      <td>0.209+/-0.07</td>\n",
       "      <td>0.227+/-0.05</td>\n",
       "      <td>0.235+/-0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Baseline Bagging   Baseline RF Baseline XGBoost\n",
       "AUC ROC                0.912+/-0.02  0.912+/-0.02     0.928+/-0.01\n",
       "Average precision      0.072+/-0.03   0.08+/-0.03     0.095+/-0.03\n",
       "Card Precision@100     0.209+/-0.07  0.227+/-0.05     0.235+/-0.07"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_test_performances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost was observed to provide better performances than random forest across all performance metrics, as was already reported in [](Model_Selection_RWD_Comparison). The performances of bagging were on par with random forest in terms of AUC ROC, but lower in terms of Average Precision and CP@100. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balanced bagging\n",
    "\n",
    "Similar to [](Ensembling_Strategies_Transaction_Data_Bagging) with simulated data, the imbalance ratio (`sampling_strategy` parameter) was parametrized to take values in the set $[0.01, 0.05, 0.1, 0.5, 1]$ for the model selection procedure. The number of trees and maximum tree depth were set to 100 and 10 (as with baseline bagging)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC Test</th>\n",
       "      <th>AUC ROC Test Std</th>\n",
       "      <th>Average precision Test</th>\n",
       "      <th>Average precision Test Std</th>\n",
       "      <th>Card Precision@100 Test</th>\n",
       "      <th>Card Precision@100 Test Std</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Execution time</th>\n",
       "      <th>AUC ROC Validation</th>\n",
       "      <th>AUC ROC Validation Std</th>\n",
       "      <th>Average precision Validation</th>\n",
       "      <th>Average precision Validation Std</th>\n",
       "      <th>Card Precision@100 Validation</th>\n",
       "      <th>Card Precision@100 Validation Std</th>\n",
       "      <th>Parameters summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.917228</td>\n",
       "      <td>0.017889</td>\n",
       "      <td>0.086163</td>\n",
       "      <td>0.022412</td>\n",
       "      <td>0.247857</td>\n",
       "      <td>0.041656</td>\n",
       "      <td>{'clf__base_estimator': DecisionTreeClassifier...</td>\n",
       "      <td>260.625890</td>\n",
       "      <td>0.913089</td>\n",
       "      <td>0.006379</td>\n",
       "      <td>0.094850</td>\n",
       "      <td>0.017160</td>\n",
       "      <td>0.258929</td>\n",
       "      <td>0.025140</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.918801</td>\n",
       "      <td>0.015721</td>\n",
       "      <td>0.090421</td>\n",
       "      <td>0.017192</td>\n",
       "      <td>0.247500</td>\n",
       "      <td>0.037422</td>\n",
       "      <td>{'clf__base_estimator': DecisionTreeClassifier...</td>\n",
       "      <td>289.001823</td>\n",
       "      <td>0.914338</td>\n",
       "      <td>0.006752</td>\n",
       "      <td>0.089633</td>\n",
       "      <td>0.017394</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.037095</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.919477</td>\n",
       "      <td>0.013627</td>\n",
       "      <td>0.087215</td>\n",
       "      <td>0.016194</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.039448</td>\n",
       "      <td>{'clf__base_estimator': DecisionTreeClassifier...</td>\n",
       "      <td>251.555288</td>\n",
       "      <td>0.914954</td>\n",
       "      <td>0.008718</td>\n",
       "      <td>0.085063</td>\n",
       "      <td>0.017055</td>\n",
       "      <td>0.241071</td>\n",
       "      <td>0.034998</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.921931</td>\n",
       "      <td>0.012261</td>\n",
       "      <td>0.076810</td>\n",
       "      <td>0.015141</td>\n",
       "      <td>0.209643</td>\n",
       "      <td>0.035962</td>\n",
       "      <td>{'clf__base_estimator': DecisionTreeClassifier...</td>\n",
       "      <td>238.826444</td>\n",
       "      <td>0.913580</td>\n",
       "      <td>0.012917</td>\n",
       "      <td>0.069417</td>\n",
       "      <td>0.010087</td>\n",
       "      <td>0.195714</td>\n",
       "      <td>0.013209</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.921426</td>\n",
       "      <td>0.013367</td>\n",
       "      <td>0.069408</td>\n",
       "      <td>0.011683</td>\n",
       "      <td>0.189286</td>\n",
       "      <td>0.023332</td>\n",
       "      <td>{'clf__base_estimator': DecisionTreeClassifier...</td>\n",
       "      <td>197.796464</td>\n",
       "      <td>0.914754</td>\n",
       "      <td>0.011732</td>\n",
       "      <td>0.060259</td>\n",
       "      <td>0.008555</td>\n",
       "      <td>0.160357</td>\n",
       "      <td>0.021556</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AUC ROC Test  AUC ROC Test Std  Average precision Test  \\\n",
       "0      0.917228          0.017889                0.086163   \n",
       "1      0.918801          0.015721                0.090421   \n",
       "2      0.919477          0.013627                0.087215   \n",
       "3      0.921931          0.012261                0.076810   \n",
       "4      0.921426          0.013367                0.069408   \n",
       "\n",
       "   Average precision Test Std  Card Precision@100 Test  \\\n",
       "0                    0.022412                 0.247857   \n",
       "1                    0.017192                 0.247500   \n",
       "2                    0.016194                 0.240000   \n",
       "3                    0.015141                 0.209643   \n",
       "4                    0.011683                 0.189286   \n",
       "\n",
       "   Card Precision@100 Test Std  \\\n",
       "0                     0.041656   \n",
       "1                     0.037422   \n",
       "2                     0.039448   \n",
       "3                     0.035962   \n",
       "4                     0.023332   \n",
       "\n",
       "                                          Parameters  Execution time  \\\n",
       "0  {'clf__base_estimator': DecisionTreeClassifier...      260.625890   \n",
       "1  {'clf__base_estimator': DecisionTreeClassifier...      289.001823   \n",
       "2  {'clf__base_estimator': DecisionTreeClassifier...      251.555288   \n",
       "3  {'clf__base_estimator': DecisionTreeClassifier...      238.826444   \n",
       "4  {'clf__base_estimator': DecisionTreeClassifier...      197.796464   \n",
       "\n",
       "   AUC ROC Validation  AUC ROC Validation Std  Average precision Validation  \\\n",
       "0            0.913089                0.006379                      0.094850   \n",
       "1            0.914338                0.006752                      0.089633   \n",
       "2            0.914954                0.008718                      0.085063   \n",
       "3            0.913580                0.012917                      0.069417   \n",
       "4            0.914754                0.011732                      0.060259   \n",
       "\n",
       "   Average precision Validation Std  Card Precision@100 Validation  \\\n",
       "0                          0.017160                       0.258929   \n",
       "1                          0.017394                       0.255000   \n",
       "2                          0.017055                       0.241071   \n",
       "3                          0.010087                       0.195714   \n",
       "4                          0.008555                       0.160357   \n",
       "\n",
       "   Card Precision@100 Validation Std  Parameters summary  \n",
       "0                           0.025140                0.02  \n",
       "1                           0.037095                0.05  \n",
       "2                           0.034998                0.10  \n",
       "3                           0.013209                0.50  \n",
       "4                           0.021556                1.00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performances_df_balanced_bagging=performances_df_dictionary['Balanced Bagging']\n",
    "performances_df_balanced_bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us summarize the performances to highlight the optimal imbalance ratio, and plot the performances as a function of the imbalance ratio for the three performance metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.915+/-0.01</td>\n",
       "      <td>0.095+/-0.02</td>\n",
       "      <td>0.259+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.919+/-0.01</td>\n",
       "      <td>0.086+/-0.02</td>\n",
       "      <td>0.248+/-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.922+/-0.01</td>\n",
       "      <td>0.09+/-0.02</td>\n",
       "      <td>0.248+/-0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters           0.1              0.02               0.02\n",
       "Validation performance     0.915+/-0.01      0.095+/-0.02       0.259+/-0.03\n",
       "Test performance           0.919+/-0.01      0.086+/-0.02       0.248+/-0.04\n",
       "Optimal parameter(s)                0.5              0.05               0.02\n",
       "Optimal test performance   0.922+/-0.01       0.09+/-0.02       0.248+/-0.04"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_balanced_bagging=get_summary_performances(performances_df_balanced_bagging, parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_balanced_bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DCfu2b99Ot27dyjy2sut36tSJnTt3JjyMTp8+nWeffTbhOps3b2bBggVccMEFPPDAA/ztb3+jTZs2fPLJJwnHdenSBZ/Px+7du+Pbdu3ahdfrlVoWQlSRYRgMHz6cN998s1SP58aNG/nb3/5GWloaX375JUePHmXJkiXcdtttXHLJJRw+fLjCN67OPvvshKka33//PUVFRbUaf1Nrd1JTU+MJsdvtTkh+X3vtNTRN48c//jGFhYXMnz+f8ePHV+n70KtXLzZv3hz/+1BKsXnz5vg0o169erFp06b48UeOHOHw4cPx/UJUlyTazdyaNWtITk7mhhtuID09Pf7xs5/9jD59+rBy5UoyMjLo1q0b99xzD//+9785cOAAH3/8MQ888AAjRoygffv2AJx22mn8+te/ZsaMGbz22mvs27ePbdu2cd999/Htt99Wex3Jhx9+mIKCAp577jkgVnBi5syZPPPMMzz77LPs3r2b77//ntdff52HH36Y++67L/7wWtuxCCHqTlXaISDeq33hhRfGh2J37dqV/v378+CDD7J161Z27NjBtGnTyM7OLlW0plhqaioffvghBw4cYNOmTfECPKFQCLfbzejRo5k9ezZbtmxhy5YtPPnkk0CsAm1N7uf3+3n00UfZvXs3K1asYP369YwbN67MYyu7/siRI/H5fDz11FPs3buXNWvWsGbNGvr3759wHYfDwYIFC1i2bBkHDx5kw4YNHDlyhPPOOy/huC5dunD55Zczbdo0vvnmG7755pt45eHiN1GFEJWbPHkywWCQiRMnsnHjRvbv38/KlSu59957GT16NH379iU1NRW/388//vEPDh48yNtvv83SpUsrnOM7btw43nzzTf72t7/x3//+l0ceeaTMQq+noqm1O927d+err74CYNiwYSxYsIANGzYwb948Fi1ahNVq5ZNPPmH8+PFceumlpeIszxVXXEFRURFPPPEEu3btir/RcOWVVwJwww03sGbNGlasWMHOnTuZNm0aAwYMoHPnzlW6vhCl1O9qYqK+XXHFFeqxxx4rc9+qVatUenq62rlzp8rJyVEPPfSQuvTSS1WPHj3U5Zdfrp5++mkVDAZLnbd69Wo1ZswYlZGRoS666CJ15513qp07d5YbQ0XraM+bN09179494fyvv/5a3XbbberCCy9UGRkZ6qabblIbNmwo8/zqxiKEqH9VbYcOHz6sunXrFl/TtFh2draaOnWqysjIUH379lVTpkxRmZmZSimlNm7cqNLT01U4HI4f//XXX6urrrpK9ezZUw0aNEi9/PLL6rrrrlMvvPCCUkopr9er7r//ftW7d2916aWXqpdeekmlp6fH12Kt6H4ne+edd9Sll16q/vjHP6revXurIUOGqLVr18b3T5s2LWFd1qpcf8uWLWrMmDHqvPPOU0OHDlXvv/9+ma/1vffeU1dccYU677zz1MCBA9Wbb76plFLqwIEDCWu/5ubmqqlTp6o+ffqoCy64QE2bNk3l5eWV+/0rK2YhhFJHjx5VM2bMUAMGDFA9e/ZUP//5z9WiRYtUKBSKH/P888+riy++WPXp00eNHTtWvfPOOyo9PV0dOnSozJ83pZR6/fXX1U9/+lN1wQUXqFdffVX17du32utol9TU2x2v16suvPBCtXXrVpWXl6duv/12df7556t7771Xbdy4UV144YXqpz/9qXrxxRdVNBpVe/bsUT6fL+H1nhxPsa1bt6prrrlGnXfeeeoXv/iF2rZtW8L+d999V1122WWqd+/e6q677lLZ2dkVfq+FqIimlEzEEkII0XL885//pF+/fvGqwN988w033ngjmzdvrvaUk3fffZdnnnmm1NBJIYQQNbdixQoWLFjAwoUL42uUnywajfLHP/6RDRs2sHLlSlwuVz1HKUTFpBiaEEKIFuWFF15gw4YN3HHHHfh8Pv7whz8wcOBAqesghBCNxHXXXUdBQQFjxoxh+PDhDBw4kM6dO+NyucjJyWHz5s2sWLECq9XK66+/Lkm2aJSkR1sIIUSLsmvXLp544gm++eYbbDYbAwcO5KGHHqp0jdyySI+2EELUnd27d/OXv/yFL774gkOHDhEMBklJSeHcc8/lyiuvZOTIkfImqWi0JNEWQgghhBBCCCFqkVQdF0IIIYQQQgghapEk2kIIIYQQQgghRC2SRFsIIYQQQgghhKhFkmgLIYQQQgghhBC1SBJtIYQQQgghhBCiFkmiLYQQQgghhBBC1CJJtIUQQgghhBBCiFokibYQQgghhBBCCFGLJNEWQgghhBBCCCFqkSTaQgghhBBCCCFELZJEWwghhBBCCCGEqEWSaAshhBBCCCGEELVIEm0hhBBCCCGEEKIWSaIthBBCCCGEEELUIkm0hRBCCCGEEEKIWiSJthBCCCGEEEIIUYsk0RZCCCGEEEIIIWqRJNpCCCGEEEIIIUQtsjR0AKciGo0SjUYTthmGUWpbYyGx1YzEVjNVic1ms9VTNKKmpJ2rPRJbzTTW2Koal7RzjZ+0c7VHYqu+xhoXSDvX1DX5RDs7OzthW+vWrUttaywktpqR2GqmKrF16NChnqIRNSXtXO2R2GqmscZW1biknWv8pJ2rPRJb9TXWuEDauaZOho4LIYQQQgghhBC1SBJtIYQQQgghhBCiFkmiLYQQQgghhBBC1KImPUdbCCGEEEIIIUTNhMNh9u/fTyAQaOhQmhyHw0HHjh2xWq1l7pdEWwghhBBCCCFaoP3792MYBu3atUPTtIYOp8lQSuHz+di/fz9du3Yt8xgZOi6EEEIIIYQQLVAgEMDtdkuSXU2apuF2uyscCSCJthBCCCGEEEK0UJJk10xl3zcZOi6EEEIIIYQQolY8//zz7Ny5k+zsbILBIKeffjqpqanMmjWr3mMJBoN88MEHjBw5st7v3ewT7aJwEYZuYDfsDR2KEELUCW/Ii8PiwKI3+yZdCNFCFQQLcFvdGLrR0KEIISoxZcoUANauXcv+/fuZNGlSg8WSk5PD6tWrJdGuC0WRInwhH2cknSEPoUKIZqkgVEDYDJPmSGvoUIQQok7kBnPxR/20d7Vv6FCEEDXg9XqZOHEiy5YtwzAMFixYwDnnnMO7775Lx44d2b9/P0opZs6cSevWrXnxxRfZunUrpmly/fXXM3DgwITrzZo1i0OHDhEKhbjhhhsYPHgwmzdvZuHChei6zhlnnMGDDz7I4sWL2bdvH6+99hq33HJLvb7mFjFH2x/1c6zoGEqphg5FCCFqnVKK3EAupjIbOhQhhKgbCvKD+RQEC37YFo2iHzsGprR9QjR2Ho+H888/ny+++IJoNMrGjRvp378/AD179uSFF15g0KBB/PnPf+bzzz/nyJEjvPTSSzz//PMsXryYwsLC+LV8Ph///ve/eeqpp3j66acxTROlFHPnzuWpp55i/vz5tGnThnXr1nHzzTfTuXPnek+yoQX0aAM4DAf+iJ9sfzZtXG0aOhwhhKh1ITOEL+QjyZ7U0KEIIUSdcFlcZPozsVvs2A07WiCAnpODstlQaTKiR4jGbuTIkfz1r39FKcUFF1wQX3+6b9++AJx33nn861//ol27duzcuZPJkycDEIlEOHr0KElJsWcct9vN1KlT+f3vf4/P52PYsGHk5eWRlZXFjBkzgNjc7J/85CcN8Cp/0CISbQC3xU1eMA+7YZcHUSFEs2Lk5eNwWMgJ5uCxeaR6qBCiWdI1Hbtu56j3KGcmnYk1Px/T5ULPzibqdoPN1tAhCiEq0KtXL5599lnWrFnD7bffHt++c+dO2rVrx7Zt2zjrrLPo2LEjGRkZTJs2DdM0eeONNzj99NPjx2dlZbFz505mz55NMBhk9OjRDB06lHbt2jFnzhw8Hg//+te/cLlcaJrWYKOa6yTRNk2Txx57jJ07d2Kz2Zg1axadOnWK71+1ahWLFi0iKSmJUaNGMWbMGACuueaa+DsVZ555JrNnz661mDRNw2lxcsx/DKthxWFx1Nq1hRCiIemBAI4iE1/7NPxRPy6Lq6FDEkKIOmE1rPgjfrJ8xzjd70e53Sil0LOzMTt0aOjwhBCVGDp0KBs2bKBLly7xbevWrWPZsmU4HA4effRRkpOT2bx5M5MmTcLv9zNgwADcbnf8+NatW5Odnc2ECRNwOp3ccMMNWK1Wfv3rX/PAAw9gmiZut5sZM2bgcrkIh8MsWLCAu+66q15fa50k2v/85z8JhUIsX76cLVu2MGfOHF588UUgVvnt2WefZeXKlSQnJzNhwgT69etH27ZtAViyZEldhAQQqz6u7Bz1xd4JleJoQohmQSmMwkLsKUnkGDm4kiTRFkI0X06LE19eJt5Q7GEahwPN60Xz+VAlHsaFEA1r+PDhpbZFo9FSFcDvvPPOhE5ZgHvuuafc62qaxoMPPlhq+0UXXcRFF11UavvixYurGnKtqpNiaJs2bYpPbu/duzfbt2+P7zt48CDnnHMOqamp6LpOz5492bp1Kzt27MDv93PLLbfwy1/+ki1bttRFaFgNKwrFsaJjUjhICNFsmHY7rtxCAuEiApFAQ4cjhBB1yhMwyYoWEoqGAFAOB/rx4xCNNnBkQojyzJo1iy1btjBs2LCGDqVe1EmXrtfrxePxxL82DINIJILFYqFTp07s2rWLrKws3G43n3/+OZ07d8bhcHDrrbcyZswY9u3bx+2338769euxWMoP0TAMWrdunfiCLJaEbWaRiTWYOFQ8jTR8YR8ha4gOng7oWv0UXz85tsZEYqsZiU00GoYB0Sh2b4ACR4FMjxFCNF/RKBZ/ANPm4qjvKB6rB5fVhT0SRsvLQ8nvPiEapUceeaTUthdeeKEBIqkfdZJoezwefD5f/GvTNOMJc0pKCtOnT2fKlCmcdtpp9OjRg7S0NM466yw6deqEpmmcddZZpKamcvz4cTpUMN8mGo2SnZ2dsK14zH6xHH8O3pC3zIfOvEge2TnZnOY6DUM3TvVlV+rk2BoTia1mmnpsFf18iabHdDhw5fvIc+aQ5kjDqlsbOiQhhKh1eiAICiwWG4Yy8Ua8FIYLQSmSDmVjs+vYXcn18mwnhBDlqZOu3IyMDD755BMAtmzZQnp6enxfJBJh69atLF26lLlz57Jnzx4yMjL461//ypw5cwA4duwYXq83Pm+7rrgsLkLREId9h4maMtRICNHE6TroFux5hYlrzQohRDOie32oE8sCaZqOw3DitLhwWlz4dZPsg/9hX/5eDnsPUxgsJGyGGzhiIURLVCc92kOGDOGzzz7j+uuvRynFU089xerVqykqKmLs2LFYrVZGjx6N3W5n4sSJtGrVimuvvZbp06dzww03oGkaTz31VIXDxmuL0+LEH/Fz2HeYDu4OUiBNCNGkKacDR2EhhfmZpNpTpUdHCNG8RKPoRT5MZxlFHzUNqysJh89HOAgBI0KmPxPlVzgMB60crXBanLIEohCiXtRJVqnrOjNnzkzY1rVr1/jnkydPji9AXsxms/H000/XRTiVKk62D3kPcbrndBluKYRo0jSHA0t2Dr5UL8mOlIYORwghao0eDAEaVJAsR0+0gTbXGdissbW1Q9EQR3xHMDSDVEcqHqtHOleEEHVKWpgTnBYngUiAw4WH6eDpgM2wNXRIogUxlYlSivh/SsW2nfgv9r8iakYxMTFNE5PYOREV+eFrFMqMbbO65Q2jlkpZrTgCfvKyD+Du4JFebSFEs6EXelGVjXg0Ym2ekZtHtE2sMJrNsGEzbETNKNn+bLL8WSTbkkm2JUvxSCEa2PPPP8/OnTvJzs4mGAxy+umnk5qayqxZsyo9d/fu3RQWFtK7d+96iLR6JNEuwWFxEIwGOeyNJdt2w97QIYlGJp4MK4WJSSgaIhgNJmwvmShHVZSoisa/NjGJmJF4QmxixhNqjdLvzmtosUS7hOIq+RoamqYl/Kmjoxka0UjsvqIFc7mxZuWQ6TxI+7Qf1dvqCkIIUWcqGjZ+EtPhwMgvwEzyoOw/PM8ZuoFbd6OUwhf2kR/KTxhWLm2lEPVvypQpAKxdu5b9+/czadKkKp/70Ucf0apVK0m0mwK7YScUDXGo8BBnJJ0hyXYTVSrxRZXqNUbFepKLE2DTNOPJccle4/ifJdZdL06MC/QC8grzYpXchMgAACAASURBVAmxUpTMlctKhDU00MDAQDN+2FfbZP6ZwDCw2dz4D+0ny2KlbVIH+XchhGjSqjJsPE7TUDYblqwcwqefVuocTdNwWpwAhKNhjnqPomt6bFi5zSPTCIVoQJFIhD/84Q8cOHAApRS33347GRkZvPzyy2zatAmlFIMHD2bgwIGsW7cOq9VKt27d6N69e0OHnkAS7TLYDBsaGgcLD9LB0wGL9sO36eQH1ZN7IUt9XeL44qSuomNaopOHSRcnw8XbHCEHvrCv9DDpE+dFzEjCsGmTWLJcXg8xkNBLXDLZ1dDQNT2eEGtoWHRLbFsZf08em4ewVaqZisZJ2W04TZOiQ/vI6WiltbtuV3IQQoi6pBd6UUbVH12VzYbu86F7vZhJSeUeZzWsWA0rpjLJDeaSHcjGY/WQYk/BYTha/HOaaDmW71jOW/95q1aveeO5NzL2nLHVOmf16tXxJaHz8/O56667WLp0KevXr2f+/Pm0adOGdevW0bZtW6688kpatWrV6JJskES7XFbDChoc8R6p8DilVEIDXN4QYIBcLZfc/NyE/WUdf/KwJf2kVdg0vfJkv+Q2Xdcr3A8QsUfILcr94Xon98yeHKNecYxALOE1o6V7jcvpJS6P3+Inz5f3Qyxl9BJr2g/Dpuuql1iIpkg5Hbh8EQoP7sboZCHVkdbQIQkhRPVFo+hFRSiXu1qnmQ4HlqwcQk4nVDK3W9d0XBYXSikCkQDekBebxUaaLQ23zS3DyoWoJ7t372br1q189913AESjUfLz83n88cd56aWXyMnJ4eKLL27gKCsniXYFrLq1VocOVbX3U6nEObknz9GtdH/x0OgTzKhZ6fFF4SKKIkVVun5l8RYrTnaLe4hL9hJbDWuVE2LpNRbi1Ci3h6TCAnIP/Q/Lj7rjsXkaOiQhhKgWPRhCUwpV3TfSDQOl6Viycoi0awN65cmypmnxAmlhM8wx/zF0v06KPYUkW/k940I0dWPPGVvt3ue60KlTJ9q2bcvNN99MMBhk8eLFOJ1ONmzYwOOPP45SiptuuonBgwejaVq5uUhDk0S7EapseHo5HeanxG6xy3x0IZox5UkiyVvI8SP/xTjj3PjcRCGEaAqMQi/KUrPOD+V0oBf5sGRmVTnZLlbc6WIqk/xgPrmBXEyHiRkxcRqyJrcQdeHqq69m7ty53H333fh8PkaPHo3NZiM5OZkJEyaQlJTEhRdeSPv27TnnnHOYP38+nTp1om/fvg0degJJtIUQoppM0+Sxxx5j586d2Gw2Zs2aRadOnRKO8fv9TJw4kSeffJKuXbsSDod56KGHOHToEKFQiEmTJjFo0KD6C1rTwO3Bk1vAMWMXHTqky5trQogmQw+GUA5njfsaTJe7xsk2nBhWbo1VOw9FQ2R6M7FqVtIcabitbllGUYhaMHz48PjnM2bMKLX/lltu4ZZbbknYdskll3DJJZfUeWw1IZNNhBCimv75z38SCoVYvnw5999/P3PmzEnYv23bNsaNG8eBAwfi295//31SU1N56623eOWVV3jiiSfqO2zQdTR3Es6sPI5l7SNsypQMIUTLYbrc6AE/lswsMCuvEVMeu8WOx+rB0A2O+4+zr2AfWUVZBKPBWoxWCNHUSaIthBDVtGnTJvr37w9A79692b59e8L+UCjE/Pnz6dKlS3zbFVdcwa9//ev414bRQL0fhoHh8mA7msmx3ANETVlvXQjRcphOVyzZPn5qyTaARbfgtrpxWVwUhgvZX7Cfg4UHKQoXNdo5o0KI+iNDx4UQopq8Xi8ezw8FxQzDIBKJYDlR0basOUJutzt+7j333MO9995bpXsZhkHr1q0TtlksloRtodQ0IpEgNns1qvGGkvD7Cgm38dMm7Ue1Vk335NgaE4mtZhprbI01LtH4mU4XelERluNZRNpWfxj5yUquyR2KhjjiO4KhGbE1ua0eLLo8bgvREslPvhBCVJPH48Hn88W/Nk0znmRX5MiRI9x9993ceOONjBgxokr3ikajZGdnJ2xr3bp1wra8vFxUJILhiFTxFcRowTB5324l+4wcUl2tcRiOU55neHJsjYnEVjONNbaqxtWhQ4d6iKb5qawWxQcffMDChQvRNI2xY8cyZsyYKtWvaCxMVyzZNrKyibZpfcrJdjGbYcNm2IiaUbL92WT5s0i2JZNsS45XMhdCtAwydFwIIaopIyODTz75BIAtW7aQnp5e6TlZWVnccsstPPDAA1x77bV1HWKVKLsdt7KiHz3GkfwD7CvYx1HfUXxhnwwpF6KFq6gWRTQa5emnn+aNN95g+fLlvPrqq+Tk5FRav6KxMV0uDF8s2aaWh3obuoHb6sZtceML+zjgPcCBwgP4wj5MdWpD1oUQTYP0aAshRDUNGTKEzz77jOuvvx6lFE899RSrV6+mqKiIsWPLXn/ypZdeoqCggAULFrBgwQIAXnnlFRyOhu3hMJ1ObP4A9uNeQu3bEoqGOOo7CoDb6ibZlozdsEtFXSFamIpqURiGwbp167BYLPFRBW63u9L6FY1RcbINWUTbtomt0FCLSg4rD0fDHPUeRdf02LBymwerXrMly4QQjZ8k2kIIUU26rjNz5syEbV27di113JIlS+KfP/LIIzzyyCN1HltNKKcDwmHsh46gt2uLzeNGKUUwGuSI9wiapuG2ukmyJeGwOGptPrcQovGqrBaFxWLh73//OzNnzuRnP/sZFoul0nMaq7pOtotZDStWI7Ymd24wl+xANh6rhxR7Cg7DIWtyixbprrvu4tZbb02ob/PMM8/QpUsXRo4cmXDsL37xC9566y1WrFhB37596d69e3xfMBjkxhtv5J133in3Xu+99x7Dhw9nz549fPrpp6WWCqttjbvlE0IIUS+U1UpU17EeO04kGCTaKg27Ycdu2FFKEYgG8Hq9aJqGx+bBY/PgMCTpFqK5qkotiqFDhzJ48GB++9vfsmrVqhrXr6hK0UdfShJWpxu9rgqLpaSgeX2YoTBm+3YVJtsWi4W0tLRTvmXxG5o+00fEiNDK0QqP3XNK7WpjLhLYWGNrrHFB446ttlx99dX87W9/iyfa4XCYzz77jDvuuKPcc8aPH1+je/35z3/miiuuID09vUrT/k6VJNpCCCFiDIOo24WRX4AeDBFu1wYsFjRNS0i6i8JFeENeFIokW5Ik3UI0QxkZGXz44YdceeWVpWpReL1e7rzzTl577TVsNhtOpxNd1ys8pyJVKfpYmF+IEYygGXX76KofOky0oCBWIK2cZDstLY3c3Nxava/f9HMsegwdnRR7Ckm2JGyGrdrXaazFC6HxxtZY44KWUfTxsssu4+WXXyYQCOBwOPjXv/5FRkYGv/vd7wgGgxQUFDBx4kQGDBgQP2fWrFkMHjyY888/n8cff5zCwkLOPPPM+P7Nmzfz2muvARAIBJgxYwZbt24lJyeH3/3ud1x33XWsWrWKmTNn8sEHH7BixQpsNhtnnnkm06ZN44MPPmDjxo0EAgEOHTrEuHHjGD58eLVfmyTaQgghfqBpmG43mj+A7fBRwu3boey2Eru1eOXc4qS7MFSIhkayLRlXsgullAyBFKKJq6wWxYgRIxg3bhwWi4Vu3boxcuRINE0rdU5TY7rdGIVegAqT7dpm1a1Y9diw8vxgPrmBXFxWF6mOVJyGU9pUUS/sy5fjeOutWr1m4MYbCZZTvwbAbrfTv39/Pv74Y4YNG8batWvJyMhg2LBhZGRksG3bNl599dWERLvYunXr6NKlC3fccQfffvstmzZtAmDv3r08+uijtG3blsWLF/Phhx9y880388Ybb/D444/z7bffApCfn8+iRYt4/fXXcbvdPPvss6xatQqn04nX62XevHkcOHCABx98sPEk2pUt77Bq1SoWLVpEUlISo0aNYsyYMfF92dnZjB49mtdee63MOY9CCCHqXvG8bduhw4TbtcX0lF6j++Sk2xv2sr9gPwX5BSTbknHb3DLvUIgmqrJaFGPHji2z+OPJ5zRFDZVsA+iajsvqAiAYDXLYexirZiXNkYbb6pbClKJZGjlyJPPnzycjI4PCwkL69evH4sWLWbNmDZqmEY2WvRLK3r17ufjiiwHo0aNHfKpK27ZteeaZZ3A6nRw/fpzzzz+/zPMPHz7MWWedhdsde8bp3bs3X375Jd27d+fss88GoF27doRCoRq9rjpJtEsu77BlyxbmzJnDiy++CEBOTg7PPvssK1euJDk5mQkTJtCvXz/OPPNMwuEwjz76aINX4RVCCFH2vO3yHjiLk+4kWxIhS4jCcCH5oXx0TSfJmiRJtxCiSWnIZLtY8ZSdiBnhuP84x/3HSbGlkGRPwm7Y6z0e0fwFx46tsPe5rnTt2pWioiLefvttrrrqKl555RVGjhxJv379WLt2LevWrSvzvE6dOrF9+3b69+/Pf//7XyKRCABz5sxhxYoVuN1unnjiCdSJ5ft0XY9/DrEh9/v27cPv9+N0Otm8eTM/+tGPAGrleaVOJtRVtLzDwYMHOeecc0hNTUXXdXr27MnWrVsBmDt3Ltdffz3t2rWri7CEEEJUV4l529Yjx+DEL7GK6JqO0+LEbXVjN+wUhgs57D3MvoJ9ZPuzCUQCCb/ohBCiMTLdboyCwjpZZ7s6LLoFt9WNy+KiMFzI/oL9HCw8SFG4SNpS0WwMHz6c999/n8GDBzNw4ECeeeYZJk2axFdffUVeXl6Z54wePZrjx48zadIk3nnnHazW2HJ5w4YN41e/+hV33nknRUVFZGVlAXD++efzm9/8Jv5zk5qayq233sqUKVO4/fbbyc/PZ9SoUbX2muqkR7ui5R06derErl27yMrKwu128/nnn9O5c2feffddWrVqRf/+/Vm4cGGV7lOVKpVmkYk1aI0Pb2xItVWlsi5IbDXTWGOzh+1YLBaSWic1dCiiOahk3nZFipNuAFOZFIQKyAvmYWgGSbakeDIuPd1CiMbI9HgavGe7WMk1uUPREEd8RzA0I7Ymt9WDpa4qsgtRD0aMGMGIESOAWI2IIUOGlDqmeOmuksulPvroo6WOu+eee8q8x4wZM+KfF1c5Hzp0KEOHDk04ruR8bLvdXuGSYRWpk5/IipZ3SElJYfr06UyZMoXTTjuNHj16kJaWxuuvv46maXz++ef85z//Ydq0abz44ou0bdu23PtUpUpljj8Hb8jbKBLtuqhSWVsktppprLH5I35aO1uTXVBxpcqmXKVS1L+qzNuuSFlJd24wF6tmjSXdNjc23SZJtxCiUSk1jLwRsBk2bIaNqBkl259Nlj8rVhsjpXrtshCi7tRJol3R8g6RSIStW7eydOlSIpEIEydO5L777mPw4MHxY8aPH89jjz1WYZItGplwGD0cQfP70f1+tFAEdA10HaVrsXeANQ2laaDpse0aoMc+B1C6HjtH0+PHA2h2B1ogcOLr4uucuK+mlf4QQtSZ6szbrkjJpDtqRskP5ZMTzMGqWUm2J+OyumQOohCi0SiZbNOIRrIZuoFbd6OUwhf28X3e9/i9flo5WuG0OGXZRSEaUJ0k2pUtCWG1Whk9ejR2u52JEyfSqlWrughD1BWl0MIRtHAYragIwx+AaBSUQhkGymJFuazxY2MfJ84zTcCMfa4ASuwHNKU4cTCggVLoRX6shYVoCpR2YnvxtTUNDVAUX+PEvpOSfKXrsQQeSiX5Std/OKdEkq80fkggTk7yi7dHo2CakuSLlqWc9bZrfDndwKn/kHTnBnLJCeRg0SykOFJwWpySdAshGlxxsq0fzwKL0ah+7xcPK0+yJ+Ev9HPUexRd02PDym0erLq1oUMUosWpk0S7siUhJk+ezOTJk8s9f8mSJXURlqgppdBCYbRQCL0o1mONacZ2WSyYVivYy3kIPumXUGUlO8rc7/FgllPWv7x4438WJ/GmQlOR+L6Tk3wtXkykRJJ/4hon+tUTk3xi51gKvNjy835I8vXiRL+MJL9kAl8yyS8+pzpJfsl9kuSLhnAK87YrYugGLj22tE3UjJLjz8HExKpbSbGn4LK4sBmnfh8hhKgJ0+VCyy/A0KjxiJ66ZjWsWI3Ymty5wVyyA9l4rB5S7Cmy+oMok1JK/l3UQGXFCKVqgijNNGO91YHgD4k1xBI9iwXT4WiUv1jiEhLUH1T0o1DTmp3K5cIMh0tsUCcl+lQxyS+Z6Ccm82Um+SeurzQSk/wTvfOmGYSk0+BEL6EQdeXkedu1OaSyZNIdMSPkBnLJVtlYDSvJtmRJuoUQ9U/TwO3Gcvgw0HiTbTixJrfFhVKKQCSAN+TFZrGRZkvDbXPLsHIBgMPhwOfz4Xa7JdmuBqUUPp+vwmWpJdEWEI2iFfkx8vLRfT60UAhNxeZTK6sF0+VqtL9EGp1yepfrIskvfSH1w0eRDy0SBpsk2qLulZy3rTudJ6Zh1G6bYdEt8Yq6ETNCTiCHLDMLu8VOii0Fh8UhSbcQon5oGlG3G0tePtC4k22IDSsvLgocNsMc8x9D9+uk2FNIsiVJ29nCdezYkf3795OZmdnQoTQ5DoeDjh07lrtfEu2W6KTCZXo4gp6cjF7oRVktKJdUrGySTh5SLkR9OjFvW8/NxxoKnvK87YqcnHRnBbIwTTOedDutTpmPKISoW00s2S5m1a1Y9diw8vxgPrmBXFxWF6mOVJyGU3o0WyCr1ZowxVfUHkm0m7vKCpdZrURdNnC7UZFIQ0crhGjKNA3lccNRb63O265IyaQ7bIY57j+O8ischoNkW7Ik3UKIutNEk204MazcGpuaE4wGOew9jFWzkuZIw211Y+hGA0coRNMniXZzU2HhMmvFhcuEEKIWnOp62zVV3FMDpZPuFFsKKWZKvcQhhGhBmnCyXcxu2LEbdiJmhOP+4xz3HyfFlkKSPUlWfBDiFEii3dSZZmxOdTCEXlSE7g/EtjeVwmVCiGapttbbrqmEpDsaJtOfSSgnhN/rjw8vL+4JF0KIU1KcbOcXxD5PS22Sz17FI4SUUhSGC8kN5uK0OGnlaIXD4pDiaUJUkzxlNDWR2DBwPRBELyqSwmVCiMar5HrboTDhtq3rbN52RYqXuvHYPRQVFpHpzwQ/OC1OUuwp2A27JN1CiFOjaURdLix5ecCJnu0mqnhNboi9UXnEewRDN0ixp8ia3EJUgzxZNHZlFC5TgNJ0KVwmhGj86mi97ZoqTroh9gB51HcUhcJlccXXmJW5iUKIGtE0oi53s0i2i8ma3ELUnCTajUlVC5cJIUQTE5+3ffgI4bZt6m3edkVKJt2haIgjviNoaLisLpJtyZJ0CyGqrxkm2/DDmtzwQ/E0i2aR4mlCVEAS7YYUL1wWRC8KoBcVxdZARgqXCSGan4aet10Rm2HDZthQSsV7ugHcVjfJtmTshl0eJIUQVVMy2S6es92MlFc8zWPzxNfrFkJIol2/TBMtECi/cJnT2WgeOoUQok40knnb5dE0LSHpDkaDHPEdAX5IuqUokBCiUsXJdm4uQLNLtqGM4mmhXByGgzR7Gi6rS9pJ0eI1nqeb5uikwmVGTi62vAIpXCaEaNka2bzt8miaFu+5iSfd3iNomobH5sFj9UhPtxCifCWTbU0jmto8lxhMKJ5mhjlWdAwdnWR7Mkm2JGxG42vfhagPkmjXpkoKl+FyEw1HGjpKIYRoFBrjvO3ynJx0+yN+CoOFoMWqlxf3dEv1ciFEguJkOycHoNkm28WKl1Y0lUl+MJ/cQC4uq4tURyqtVKuGDk+IeiVPBDVVVuGySCyJlsJlQghRNY153nZ5Tk66i3twFAq7YSfFloLD4pBeHCFETHGynd0ykm04UTzNGiueFoqGOOI9QsgWgiB4rB55U1K0CPKvvKqkcJkQQtSNRj5vuyIl53RDbNhkViALU5lYNStJtiRcVhd2wy7L4AjRkmkaUbcbS/aJOdstINkuVtxG2nQbRwNHyfJnkWyLDSuXJcJEc9Y0nmQailLohYXoPilcJoQQdaqJzNuuTPGwSYCoGSU/lE9uMBdd00myJuGyuXAYUkxN1K9wOMzOnTspLCwkOTmZs88+G5ut6f18NXmaRtTtapHJNoChG7gsLpRSFIWLKAgVYDNspNlixdOk3oVobiTRroCRk4uRl4+y26VwmRBC1IOmNG+7MoZu4NRjBYJMZeINeykIFQDgsrpIsiVhN+wyhFLUqY8++oinn36azp0743K58Pl87Nmzh6lTpzJ48OCGDq/lKZFsK03DTElu6IjqnaZp8WXAImaETH8mml+L9XLbY+2iEM2B/HYvh17oxZKXT9TtlgRbCCHqUVOct10ZXdPjD5ZKKULRUHytbrthx+K2EIqGZF63qHUvvfQSf/nLX/B4PPFthYWFTJgwQRLthnIi2bZm5RCGFplsFyu1RFgwF6fFSStHK1lKUTR5dZJom6bJY489xs6dO7HZbMyaNYtOnTrF969atYpFixaRlJTEqFGjGDNmDNFolEceeYS9e/diGAazZ8+mY8eOdRFepbRAAGvmcaLSiy2EEA2jCc/brkyped3RWDG13IJcbBZbbIi51YVNt8ncRXHKwuEwDocjYZvdLjUDGpymEXU5Jdk+IWGJsGiYI94jGLpBij0Fj80Tn5IjRFNSJ08t//znPwmFQixfvpwtW7YwZ84cXnzxRQBycnJ49tlnWblyJcnJyUyYMIF+/fqxY8cOAJYtW8YXX3zB7Nmz4+fUq3AY69FMog4H6PIumhBCNJhmMm+7MlbDisfqIWwLEzEj5AZyyQ5kY9EseGwe3FY3dsMuPTuiRsaOHcuoUaPo27cvSUlJeL1eNm3axPjx4xs6NKHrkmyXwWpYsRqxJcLygnlkB7LxWD2k2FOkeJpoUuok0d60aRP9+/cHoHfv3mzfvj2+7+DBg5xzzjmkpqYC0LNnT7Zu3crw4cO57LLLADh8+DBt2rSpi9AqZppYM7NQut5sek6EEKKpa07ztitTPIwSYvO6C0OF5AfzgdiSOMXzuqVokKiq6667joEDB/LNN9/g8/nweDzcfffdDfOcJUorTraPZ0uyfRJd0+O93MFokMPew1g0C2mONNxWt7SDotGrk2zS6/UmzAUyDINIJILFYqFTp07s2rWLrKws3G43n3/+OZ07d44FY7Ewbdo0/vGPf/Dcc8/VRWjlUwojOwctFMR0Nd+HOCFEaV6vl08++YRQKBTfds011zRgROJkzXHedmVKPmQqpQhEA3h9XgCcFidJ1iQcVocMqRSV2rJlC//v//0/vF4vycnJBAIBrrjiCukZbCx0PVYgLSubiK5hJiU1dESNjt2wYzfsRMwIx/3HOe4//sMSYRZH5RcQogHUSaLt8Xjw+Xzxr03TxHKihzglJYXp06czZcoUTjvtNHr06EFaWlr82Llz5/Kb3/yG6667jrVr1+Jyucq9j2EYtG7dOmGbxWJJ2GYWmViD1kp/CLW8fAw0VIfTq/Vaq8OwGKSmNM6lHCS2mmmssQX0KBbDQupJPx+ibHfddRft2rWjQ4cOAPLw2Vg143nbldE0Lf6gCbE5jJn+TPDH1qhNsiXhtDhlXrco5fHHH8c0TQYMGIDb7cbn8/HJJ5/w6aef8uSTTzZ0eKKYrmO6XFgys4iAJNvlKFk8zRv2kh/Kx27YSbPHlgiTKTaiMamTJ5SMjAw+/PBDrrzySrZs2UJ6enp8XyQSYevWrSxdupRIJMLEiRO57777WLVqFceOHeOOO+7A6XSiaRqGUfGQkGg0SnZ2dsK21q1bJ2zL8efgDXkrTLQ1vx/r4aOxJbzy82v4qiuXmpJCXh1e/1RIbDXTWGMLeQtIiUZK/XycrDixbOmUUvzxj3+s8vGVFXwE8Pv9TJw4kSeffJKuXbtW6RxRBS1k3nZliucwQmx5nJxADqYysWpWPDYPLqtL5jIKAP73v//x5ptvJmwbNGgQ119/fQNFJMolyXaVJRRPM8McLTqKgUGyPdbLLSs4iMagTt72GTJkCDabjeuvv57Zs2czffp0Vq9ezfLly7FYLFitVkaPHs348eMZP348rVq1YujQoXz33XeMGzeOW2+9lYceegi7ve7X0dNCseJnpsMpxc+EaKG6devG1q1bCYVC8Y+KlCz4eP/99zNnzpyE/du2bWPcuHEcOHCgyueI6lFOB0rXsR0+gu71VX5CM2bRLbgsLjxWDxbdQkGogMPew+zN30tmUSZFkSKiZrShwxQNxDRNvv7664RtX331FVZr05ly4HrtNX78wKO4tv+noUOpeyWSbb2wsKGjaRKseqygpN1iJz+Yz/6C/Rz2HqYoUoRSqqHDEy1YnfRo67rOzJkzE7Z17do1/vnkyZOZPHlywn6Xy8Wzzz5bF+GULxrFknkcZVjAIgUVhGipvvzySzZs2BD/WtM0/u///q/c4ysq+AgQCoWYP38+Dz74YJXPEdXXEudtV8bQDZz6D/O6i8JFFAYLQQOX1UWyLTm2brfeMobcC5gzZw6zZ89m6tSpQOwZ7dxzz+WJJ55o4MiqLtyzJ8nPHOXHd/+G3J8P4eikW4m0Sqv8xKZKerZrRNd0XNbYlNNQNBRbIkwzSHWkxt+IFKI+tdx/ccXFzyIRTKezoaMRQjSg999/H6UUOTk5pKamVjptpaKCjwB9+/at9jnlqUotilBqGpFIEJu94Qs5NkjdAqXQfEWYgSBmu7blztu2WCwJNUEak7qOTSlFyAwRiAYIqABOi5NkezJOq7PSIZYn/3trLBprXI1Nx44dG2a51Fo0ffVqIhf/hLkpybRZvpLkTz7j2K3jyR49svnWaUhItjXMJE/l54g4m2HDZtiImlFyAjlk+bN+KJ4m02pEPWmmrVPljLx8DK8P093wD6ZCiIb1xRdf8NBDD5GUlERBQQFPPPEEP/3pT8s9vqKCj7V5DlStFkVeXi4qEsFwRCq9Xl1ryLoF2rHjaMezyp23nZaWRm5ubgNEVrn6jq3ILOKoeRSlFBbdP6RWsgAAIABJREFUQrItGafFid2wl3oAPfnfW2NR1bikFkXT9+233xLy5XP0+T+QO/wKTn9mAac/9zKtVq/n4EP34z+3W0OHWDeKk+1jmSd6tiXZri5DN3DprvgIn4JgATaLjTRbrHiaLBEm6lLLS7SVwsjJxZKfT1SW8RJCAM888wxvvfUW7du359ixY0yePLnCRLuigo+1eY6onpa03vapsurW+LJgUTNKXjCPnEAOuqaTZE3CZYsVU5MKvk3f+PHjCYfDCduUUmiaxrJlyxooqpoLdTyTfU8/SdKnn3P6vAV0/s0j7Hr1ecIdTmvo0OqGrmO63Vgyj0uyfQo0TYsXRo6YETL9mWh+jWRbMp4U+Z6KutGyEm3TxMjOwSj0xpJsGTYihCA2PLt9+/YAtG/fvtJCjEOGDOGzzz7j+uuvRynFU089xerVqykqKmLs2LFVPkfUPpm3XX0l53WbyowvmQPgtrqxJ8XWrpX5jU3Tb37zGx555BHmz59f6bSYkipbKWHNmjUsXrwYwzBIT0/nscceQ9d1rrnmGpJOzCk+88wzmT17dq2/JjSNwv6XsLdzJ358+xQ6PfwEu1/8E6oeiug2iPgwckm2a0PJJcIKw4V8n/c9fq+fVo5WOCzyBqOoPS3nt2Y0iuV4NrrfL8PFhRAJPB4PS5Ys4Sc/+QlfffUVKZXMMa6s4GOxJUuWVHiOqCMteL3tU6VrerzXRylFMBrkUOEh8grycBgOUmwpOKyOeG+4aPx69erF1Vdfzc6dOxkyZEiVzyu5UsKWLVuYM2dOfK53IBDgmWeeYfXq1TidTqZOncr/Z++8A6Mo8z7+mdlekk1PCKEkQEAQRECKVAUFPKogCCJK0XtV9KynwIkFRTyE88RyiqBHB2mCCCoKKlhOVLACSi/p2U2yyfaZ949NlgRIgyS7CfP5Z3dnnpn9bbLz7HyfX9u5cye9evUCys59tYm7SWNOPvl3mj/+FI3nvcKpmY823IU1RWzXOCUtwsw6M0UFRaTZ0xAF0V88TWtW5jmFS+byWLLxetGkZyI6nf5e2QoKCgqlmDdvHmfOnOFf//oXaWlpire5IVDcbxuXG+2ZdARXxS3bFM5HEAR0Kh1h2jDMGjOyLJPpyOR43nFOFJzA5rLh8rmU9jn1gKlTp1ZLZEPFnRK0Wi2rV6/GUFxM1uv1otPpOHDgAA6Hg8mTJzNx4kT27dtXI/anpKTQtHHiBfcV9OxOxuTbidy+g6gNW2rk/UIWUUQyGFBnZl32bQ1rGo1Kg0lrQqfWYXPZOJ5/nPTCdKVFmMIl0eCX+AW3B92ZDFBpleriCgoKZUhPTychIYHs7GzGjBkT2J6bm1upV1uhflA6b1tQopkuCY1Kg0bl9/B4JS+5jlwkJDSCBrPWjFFjRKfSKWGXIcrx48c5cOAASUlJtGvXDrvdzqlTp2jTps0Fx1fUKUEURWJiYgC/97qoqIiePXty6NAhpkyZwi233MKxY8e466672L59e5UKP1bEvHnzSP/5q3L3Z945HsOBQyS+8h+cLVMouurKS3q/kEal8ovtjEy8xCm1KGoYURAxqP16weVzccZ+Bo2gIVLvL56mpNAoVIcG/20RCgpAlpD1+mCboqCgEGK88847TJ8+nVmzZiEIQmDVWhAEli5dGmTrFGqKkrxtVXoGKgElb7sGKMlxBH9ed747nzyXP6/brDVj1pjRqXRKRd8QQJZlnnnmGXJycujRowc//fQTCxcu5JlnnmH27NksXLiQqKio846rrFOCJEnMmzePo0ePsnDhQgRBIDk5mWbNmgWeR0REkJWVVWnl98raGHolL/tUuVi0UYRrLZg1RlRC2e9W/rznMIyfTPOn5pC+5l1/q79aIihtDM/F50NwOPBFRCCXCiO/nNsYXiyV2eWVvDh9TvLkPCx6CxadJZBiUxe2KW0M6y8NXmgDyKJyQ6WgoHA+06dPB8rmE6alpSntgBoiKhWyyYQqLU3J265hSnuAZFnG4XVQ4C4AwKA2EKYJU/K6g8jKlSuxWCxMmTKFJk2aALB//37mz5/PE088wWuvvcaTTz553nGVdUqYNWsWWq2W119/HVH0RzGsW7eOQ4cO8fTTT5ORkYHdbic2tnLBW1kbwwFzB/Bb1G9l9utFHWa1iV7RXXg09a+Ea8wUzf4HLf76NyL+9neOLpyHrKmd71ww2xiWwedDPHgIb/xZz7bSxrD6VNUuWZY5mXeSY/IxdCodkTq/l7s2o3iUNob1G+UuQ0FB4bJn6dKl6PV68vPz2bBhA7179w6IcIUGRHHetuBwoj2TXm6/bYWLpySvW6fyV392+9xkOjKRHTI6lY5wbTh6tT6wX6H2+fDDD1m8eDF33XUXR44c4aqrrqJDhw788ssvtG/fvtyaFBV1V7jyyitZt24dXbp04Y477gBg4sSJjB49munTpzNu3DgEQWDOnDmXHDYOYPrRRLK5MRMnjqJIcmH3FVHoLSLHbWVL+g6+yv2BZ9o+RM+ULpya/gjNZj1Po3//hzOP3n/J7x3SlISRlxRIU8LIa5WS4mkAHslDelE6IiIWnYUwbRhalfJ7olAWRWgrKChc9mzdupVly5YxdepUtm7dGrhxrA+oDx0ietNWsocMDLYp9Qal33bdoVVpAzefHslDjjMHSfbndYdpwwJ53YISyl9rqFQq9Ho9cXFxTJ8+HYPBwFtvvUX37t0ByhXClXVXOHDgwAWPmz9/fg1Zfha1V03UaQs3Jw5EUJW1d0LTkcz8dR73/DiTWxrfxCN97sY4/hZiV76H44pUrH9p4HOjSoWk16PJyMQjxEMIhmY3RDSiBo2oCaTOWJ1WjBojEboIDGqDMqcpAJdL1XEFBQWFChAEgaysLGJiYhAEgbxQCAmsIm9NmULSy//B/ONPwTalXiFrNPh0OjQZWahyckGpKlvraEQNRrURs8aMWlST587jtP00R/OOkl2UTZG3CEmWgm1mg6MkrPvUqVO0bduW5ORkXnjhBU6ePAlQ7ysqtwtPZU3X17ij2WjWnd7GqG//j61jumDv3JHE+QsxHDgUbBNrH5UKn8GAJj0DQalGXqeUpM6YtWa8kpe0wjSO5x/H5rLhlbzBNk8hyFQotH0+X+B5YWEhXq/yhVFQUGh4dOvWjQkTJjBhwgTmzJnDjTfeGGyTqswnMTGkazQ0WrxcEYvVpVS/bU16Jii/cXWGSlRhUBswaUzo1XrsHjtpBWkcyztGemE6hZ5C5Sa1htDr9WRmZtKjRw8effRRfvrpJ7755htycnJwOBw1EtodbHQqLY+0uot3uryEgMDkfU/w94mN8URF0nTGs6istmCbWPsUi21VWjpiYVGwrbks0aq0mDQmNKKGXGcux/OPk1GUgcPrqPcLWgoXR7lC+9ChQwwaNCjg2fn6668ZNGgQf/75Z50Zp6CgoFAXPPTQQ+zcuZNOnTrx2GOPcd999wXbpCrTqn173u/QDtOBPwj/Yk+wzal/KP22g44oiOjVekxaEwa1AbfPTXphOsfzj3Oq4BT5rnzcPuX/crFMmjSJ5557jvvvv5/evXuzcOFC1q1bx7x581i8eDFDhgwJtomV0q5dO1JTkisd1yniStZ1e4MxSUN4w7qV0WMERJuNpk/NuTwW0lQq5GLPtljkCLY1ly0qUYVRbcSoNuLwODhjP8OJghMUuArwSb7KT6DQYCh3GfP5559nwYIFgV6yAwYMICoqiueee4533323ruxTUFBQqDWeffZZZs2axdixY8/Lp1q9enWQrKoezz77LJm//Q/n5GnEv/Uu+T17gFppqVRdlLzt0EAQhPPyurOd2UiShEalIVwbjkFtUIqpVYNu3bpx5swZpk6dytixY3niiSfIz89n2bJlFBYWMm3atGCbWCnPPvtshX20S2NUG5jZZhrXx/Zg1m8LmPoXL+9u3E/L2+8ia/JE8vr3BbEBZ06WhJGnpeNplIBkNATbossWQRACbcC8kpdMRyaCQyBcG06YLkyZxy4DyhXakiTRvn37Mts6deqEx+OpdaMUFBQU6oJ7770XgAULFiDLMoIg4Ha70WrrWeVQtYq0ybeR/PSLRG7fgVUpjHZRlPTb1mRk4XW5lH7bIUBJwSHw36hanVZyyEElqJAMEkgovbqrwMiRI+nduzfbtm3j22+/xWAwcMMNN9CzZ89gm1Zr9IjuzIYeb/HPqDcYpvuEOZ+lceUzc8l6ZzG2u6bi7Ne34V7fKhU+vd+z7UmIV8R2CKAW1ahFNbIsU+ApwOa2oVfpidRHYlAbarVFmELwqFBoXwglT/vyxiv5sHnyyPXkYXXnkeu2YXXnYfXYyC1+ne+1IyCgEkRUggpREFGf86gSVKgQUYkqRM7Zfs6jyWDE4/KUOk6FShT9jxc67zmPF3zfivafd7wKtSAi4t+mVJJsOMTExACwZ88e/vzzT2bMmMHkyZMZNmwYjRs3DrJ1VWPatGk483N5/vEHKLqiNXFLlmK74TqlbdXFUipvW+m3HVqU3KgCSLJEriMXk8+EUTQG2bL6QUxMDDfddBMulyvYplSbadOm4bBlMfepJ6p1XJjaxOx2j/JT0hDm9v8Uw45PeXRHFq2ffIE/mr7KgYnDaHzDrWgaYlsmtQofejTpGbgbxSMbFLEdCpRpEebzkG5PRxREIvQRmLXmwMKiQsOg3LuHPn368OKLL3LvvfcSFhZGYWEhr776aqAdhELDoEQ4p+Vlcjz3tF80u/PI9diwuovFs6dETOeR5ym44HkEBCI04URqLVjUYSCAU5KQZB9e2f/ok334ZKnMoyRLeGUfEhI+yYePsttDEbGUUFeLKoRKFgrKW0ioVOBXdt7A8efsQ4XRJzC1VVsUP0/VWLVqVSBU/M0332TChAmMGDEiyFZVjbS0NNxFBSAIpN8zmZQHHid64xaybx0VbNPqL0q/7ZBHFETFA1RNnn76ab744gvi4uICETz1JUUmLS0Nd+HFd4PoYGlDB0sbfG3+j9237uPjzasYuukXhj63gm//u4pNt3SmSd+b6RJ5FeqGFCFRLLa1aYrYDkU0Kg0alb9FmM1lI8eZg1ljJlwXjkGltAhrCJQrtO+++24WLVrEyJEjcTqdWCwWRowYwZQpU+rSPoVq4pG82Eq8zZ7SHuezz0uEc4n3+UKUCOcorYVITQStzMlEaSOI1Fj827QWojQRRGotRGojiNCEoRJq/sdJkiXM4WZybdYqCfTz9pcj9Esf7y1+feHjy9lffJxGo6bI5Sh/AeHcxQV8pfZ7cUvuihcgil9XdzFCLajo32EILQ0xNf4/aYiIoohO58+V0mg09fbHrbBTRwq6diZ22WpyhwxS8owvESVvW6Eh8dNPP7Fjx45Au6/LEZWgomtsZ5jSGeuEIo6tW0K71R/z7ILv2LnhO2beaCai6/UMSujHVZYrGsZijiK2Q56SFmEALp+LM/YzaAQNkfpILJIlyNYpXArlCm1BELj77ru5++67AyufVUWSJJ5++mkOHjyIVqvlueeeo1mzZoH9mzZtYvHixYSFhTFy5EhuueUWPB4PM2bM4PTp07jdbu655x769+9/aZ+uAVAinM+K5FIh26We+7eXL5xFRCyaMKK0FqK0EaSak4kMCOcIkiIaofNo/GJaa8FSS8K5uoiCiFbUoA/RghERFgu2IPdcPncRwCdLSEUFtI5oSWjGBIQe/fv3Z/z48XTo0IFff/2V66+/PtgmXTTpd0+i1dRpGJcv48DE4SQZGwXbpHqNkret0FBo1qwZLpcLgyK0AFDrjETfNo2M0XdTtGkzXZet5MO37Hz82RZmXreZUymxDEzoy+D4flwR1rLeLsACitiuR+hUOnQqHV7JS5YjC5/Nh8lnChSIVKhfVJh4tmTJEtasWYPD4UCj0TB+/PgqebR37NiB2+1mzZo17Nu3j7lz5/LGG28AkJuby7///W82btxIeHg4d955Jz169ODbb78lIiKCefPmYbVaGTlyZIMU2h7Jg9WTj7VYOOeUhGWXDtEu5Y0uqEA4R2jDAx7mNmEpRGosZcSz3xvtfx6uMVconENBMCpcHCUhlKWzetwqpWhhdbj33nu57rrrOHr0KCNGjKBNmzbBNumicbZpRc51PYlbu4lejTYz5qo7uKPZ6JBYOKu3KHnbCg2AtLQ0rrvuuoDjoz6Fjtcmsk5L/tjRFAwfQvSGLVy3fA3fLSpgd3sPf+u1kf/Gr6OpIZFBCf0YFN+XlubmwTb54igtthMTkPX6YFukUAElNSkkJCT5wnWzFEKfcu8U3n33XY4ePcr69esxm83Y7XbmzJnD22+/zdSpUys86ffff0/v3r0B6NixI7/88ktg36lTp2jTpg0REREAtG/fnv379zNo0CAGDjxbKVelqh83hSXC2e9xtpXrcbZ68rB68skvJ8e5RDiXhGOXCOcSD3OkJqKMeLZowhpGSJOCQgiQkZHB4sWLsVqtDBw4EJfLxVVXXRVss6pE586dKcpNL7Nt7gADL+ySeX23hRHGJXye/S3Pt/s7SYaEIFnZAFDythXqOfPnzw+2CRdN586dKcw6XavvIev1ZI+/hdwRfyF67SZ6rF7H9z/7+KVHKs9dp+Jtx2reOrqSlqbmjGw+kL6WbjQ11o+imQHO9WwrYltBoVYpV2h/9NFHrFixIpDLYzabeeaZZ5gwYUKlQttut2M2mwOvVSoVXq8XtVpNs2bN+PPPP8nOzsZkMvH111/TvHlzTCZT4NgHHniABx98sFLjVSoV0dHRZT+QWl1mm1wYiUNyozWFV3q+0uS5CziQf5gzRenkuGzkuqxku6zkumzkuIsfXVbyPRf2OKsElT9MWxdJtC6CJmGJxOijiNRaiC7eFq2NJEoXQYwuCos2uMJZpVYRYQnNPBDFturjFH2oVWoizrk+FC7Mk08+yaRJk3j99dfp0qULTzzxBGvXrg22WVVixowZZB76Hrm4I8R+22/M93xK3xtSGP7xEXYndGfQ1fsZ/c3/8XjqPYxIvLF+h0AGmdJ5294wM7LRiKTVKB5uhZBHpVIxZ84cDh8+TPPmzZk+fXqwTaoyM2bMqHIf7UtFMhrJunM8OTcPJXbNBq5Yu5FV37p4uX9Plv+lKSvlfcz79U3m8SbtwlMZHN+PG+P7kKCPrRP7Lhm1Ch86RWwrKNQB5d4ZaDSa8wpmaDQa1FW4mTCbzRQWFgZeS5IUOM5isTB9+nTuv/9+EhISaNeuHZGRkYA/rOm+++5j/PjxDB06tNL38fl85OTklNkWHR1dZpvVasWZb0PrlS94DkmWOOlI42DBEQ7ZjwQe05yZZcapBJGIEg+zxkJrUwpRkRHF4dqWc8K1/aHa5wrnC4Zny4AT8p0X9nTXFaEcOq7YVn3c9nwsPu9518e5NGqk5O8CuFwuevTowRtvvEFKSkqgMFp9wy25eer3fxGviyHmiXnk6BfTc+OH/KQdwtgex3nq9wXsyv6aWVc8SLQ2Itjm1ltkjQafSoVY5EAosCPIMpJWg2QyIRn0yBoN1JOoLIXLh3/84x+MGzeOa665hv/973/MnDmT//73v8E2K2SRwsPIuOsOsm8ZQezK94hbv5lHPtvD5Jtu5NjUB1lh/5bt6Z/z0h9v8dIfb9Ep4koGxffjhvjeoT+/lojtM+lKGLmCQi1SYTG0nJycMt7h7OzsKlWr7NSpEzt37uSmm25i3759pKamBvZ5vV7279/PihUr8Hq9TJo0iYceeojs7GwmT57MrFmz6NGjxyV+rIrZn/c7m898zEH7Uf6wH8XhcwJ+Md3cmMRVlraMSRpCqjmFJEMCUVoLYerzhbOCgkLDQKvV8uWXXyJJEvv27UOrrT8hwVOnTsVlt/LSrL/z1tFVHCk8wesdn8OkNXPm0ftBFEhe8wEfCKN4aUgP/n3kHUZ981eeuuJBrout3bm2QSOKyDodgSVcrw8xrwC11YYsgKzT4TOZkPU6ZK1WKaCmEHRcLleg9s2AAQN45513gmxR1Zk6dSrOvBz+NWdWnb+3L8JC+r1TyR4zktjla4h6/0Ou3v4pzYYOYurEZzhsdPJR+udsy9jJnIOvMvfg63SL6sig+H70j7uWcE1YndtcJRSxraBQ65QrtO+55x7uuusu/u///o+mTZty6tQp3njjDR5++OFKT3rDDTewZ88ebr31VmRZZs6cOWzZsoWioiLGjh2LRqPh5ptvRqfTMWnSJKKionjuuefIz8/n9ddf5/XXXwdg0aJF6GvwwvdIXt46uoJFR1djVOlpE9aSkYmDaG1OoXVYCimmpiFb3VpBQaH2mD17Ni+++CJWq5UlS5bw9NNPB9ukKmO1WnEXFXDQfpQlx9YwtNEAesVc498pipx55H4QROJWr+fv8s10v3MhM379J3/b/zQjEwfx99S/YlIbg/shGgJqFbJadbbSv9eLOtcGSCD7w1Elk9EvzjVqRXgr1Dk+n4+DBw/SunVrDh48WK9SSKxWK+7C4Eb+eWOiSXvwXrLHjSZp1XqiNm4h8oOPiBk5hJQJY7k7ZTx/2I+xPX0X2zJ28dTvC5h94BV6RndhUEJfrovpgVEdYtW+1Wp8oIhtBYVaQpBl+cIx1cAff/zB6tWrOXnyJAkJCdx66620bdu2Lu2rELfbXWnoeO6pQzhtmaRjZ/ov/+Tn/AOMaHQjj7e+p85vLkM1zBgU2y6WULXNbbfSpE1HfNqKaxMooeN+XnjhhZDNV6xsnhs1ahQuRz6O2xxkeqxs6vEWFs05/3dZptG/3yBm3ftk3zKCE9Om8PrR5bxz7D0aGeKY3GwMPaO7kGiIv2R7Q/WagCDaJssIHg+C1+NPFxJFfCbj2fxujYbIyEisVmvd21YFQtU2jUmDwWPAqKn4t1yZ5/z89ttvPPnkk2RmZhIfH8/s2bO54oorgm0WULV5zl2Yx5KF8xBUwa+HEGGxUPjbAeLfXUHER58i6bTkjB5B9rhR+MLDkWWZX/MPsT1jF9szviDTlY1e1NE3thuD4vvRK/oadLXUrumi5jmvF9HtwpPYCLkWU6dCdS4JVbsANGYNJo8JvbriRRBlngtNKpytWrVqxZNPPllm2+eff07fvn1r1aiaRJZlNmfsZN6xd1ALal5qP5Mb4/sE2ywFBYUQ4vDhw+Tn5xMeXr2iiaFCRqtsThdmML/9P84X2QCCQNrf7gFRJGbtRpAk/vbgvfSJ6cbTv/2L2QdeASDZ2ISe0V3oGdOFzhHtlQifmkIQkLVafwg5gCQhOlwI9uJaJioVok9CcLv8Y5T8boVaoG3btqxfvz7YZjQYPI0bcWrmo2RNGEPckhXELVtN9IbNZN86iuwxI7nS0porLa15uNVd/Gj7le0Zn/Nxxhd8lPEFJpWR6+OuZVB8P7pHXY1GDPLigVqNBGjOpNW62FZQuJwo98resGEDCxYsQK/X88orr9CkSRP+8Y9/cOTIkXojtIs8RTz64zN8lL6LayI78Hy7x0jQxwXbLAUFhRDj8OHDdOvWjcjIyEAdit27dwfZqqrhMDo40yaT66O7c0N87/IHCgJp9/8VWRSJXb0ewSfBw/exqccijhWdZHfOXvbk7GXt6Q9YfnIjOlFLl8gO9IzuQq/oa2hmbFyvQk1DGlFE1mmRKRbePh9ivh2NNRcBkLRaJLMJqSS/uwq1URQUyuOBBx7glVdeoVevXuftqy/zXCjjataUk89MJ3PircQvXkb84mVEv7eJrPG3kDNqOKJBT+fI9nSObM/jqffwnXUf2zM+Z0fmbrak7cCiCeOGuN4Miu9L58j2qIQgLbQpYltBocYpV2i/8847bN26laysLObOnUtmZib9+/fnpZdeqkv7LokfMn/g04zdTGs6nimtJgRv8lJQUAhpdu7cGWwTLpqErgkckg8yvcXdlQ8WBNLvuwtEkdiV74EsceaR+0k2NSXZ1JTbm96Mw+fke+vP7CkW3v889B/+yX9obEjwe7uju9A18iolr7smUamQDXokt7/NJV4foi0ftSQhIyMZDEhmI7JWh6zVKPndCtXilVf8ESv1WVT36tULe8aJYJtRIa4WyZyYMwvDgUPEv72URv9ZQsyaDWTdPpbc4UOQdVrUoooe0Z3pEd2ZmW2m8VXOD2zP2MXW9M9Yd/pDYrRR3Bjfh8HxfelguaLuFzcVsa2gUKOUK7QjIiKwWCxYLBYOHz7M008/XW882SX0TOzJdwO348vPVUS2goJCufzwww8888wz5OTkEBcXx/PPPx8yuYuVsXjaYk4f/BaNVMUbMkEg/Z4pyCqRuGVrECSJ04/9LeA1Naj09Iq5JlBQ7ZQjnT0537Eney9b0naw9tQHqAU1V0e0K/Z2d6GVOVnxdtckpQuryTKC14s6OxdkGQThbGE1rdYvvBUUqsB3332Hw+FAlmVmz57N3/72tyq1Ug0FHnrooTrro32pONqkcuyl5zD+/Cvxby8l8ZU3iV21nsyJt2IdMsjf/g/Qilr6xXanX2x3HD4nX2b/j23pu1h3eisrT24iUR/PoPi+DEroR2tzSt3NsYrYVlCoMSps71VCYmJivRPZ4P8MGlF9tgqsgoKCwgV47rnnmD9/Pi1btuTQoUPMmjWL1atXB9usKiEIAlpRgyx5q3MQGXdPAlFF3H9XYvjtIDmjh2O74brzqs4mGRIYmzSUsUlD8UgefrT9GvB2v/znYl7+czGx2qhAbvdAQ/37rQhpBAFZowncnCPLCC436qJCQACVCp/RcLawmjr4haIUQpN58+bx0ksv8cwzz7Bq1SoefPDBeiO06yNF7dtx9N8vYvphH/GLltJ4/qvErnyPzDtvwzpwAKjPOoAMKj03xvfhxvg+2L2F7Mz6mm3pu1h6Yj1Ljq+luTGJQfH9GJzQl2RT09o3vpTY9pnN/jaFGg2ySuWfY5SFVQWFKlHuL7LNZmPPnj1IkoTdbi8TcnShPB8FBQWF+kpYWBgtW7YYvQFdAAAgAElEQVQEIDU1tUbbCtY2t912G+7CPF6d82Tlg0sjCGRMnYgrKZGY1etJevFlEl5/G+uQQeSMGIKn8fkVTDWihq5RHeka1ZGHWk0l05XDV8Wi+9Osr9iU9jGP/zyX9pbWgTDztuGtlIiimkQQyuZ3SxJikQMhv6A4v1vj93gb9EphNYUy6HQ6oqOjUavVxMbG4na7g21SlbnttttwFVh5Y/7zwTal2hR26siR16/C/O1e4t9eStILC4hdtoaMyRPI69/3vGvUrDYxtNEAhjYagNWdx6eZe9iWsYs3j67gP0eX09qcwqCEfgyM70uSIaH2DFerkQQB0eE8W7ixGFmrQdLpkPV6ZLUKWa32fw5FgCsolKFcod2uXTs++OADwF+pcuvWrYF9itBWUFBoSERHRzNz5ky6d+/Or7/+iiRJrFmzBoCxY8cG2bqKcTqduF0XecMsCNgG34Bt0ACM+38hev37xKzdQMzq9RRc242cUcOwd7m63GJccbpoRiQOZETiQLySj1/zD7K38Bc+Pb2bN44s5/Ujy4jQhNMjqhM9o7twbXRnYnRRl/BpFc5DFP29uUvCO73+wmqqvHxARtbp8JlMAY+UUljt8sVkMjFp0iTGjx/PihUr6lU7IKfTWa8WBs5DELB3vwZ7ty6E7f6a+LeX0vTZF3EuW03G1Ink9+l5QZEaqbUwOukmRifdRJYrh48zvmR7xi7+/ecS/v3nEtqHt2FQQl8GxvclThdd83arVMgqFWX6AMuyv4BjkQOhwI4gg1xsuqzT+gW4ToesUZ/1gCsoXKaU++1/4YUX6tKOkEJwu9GdOIXu+AkEtwfJaEDS6/0FaQx6/2uDAZ/BgKzXKSt4Cgr1nJSUFACOHz+O2Wyma9euZGVlBdmqOkQQKOrYnqKO7UnLzCL6/Q+J3PwhyXu+wdUkiZxRw7AOHoBkMpV7CrWo4qqItvRt1oMpjcdgdefxde4PgTDzbRm7AGgT1pKe0Z3pGX0NV1muCH5bm4ZGcX534MbY60Wda0OQJWQozu82KIXVLkNeeeUVTpw4EUiRueWWW4Jt0uWHIFDQ+1oKenbHsvNL4pYso9nM2ThSW5IxdSIFPbqWe03G6qK5rekIbms6gtOOdD7K+ILtGZ8z79CbvHToLTpHtGdwQj9G6W9CpBava0EAtRpZrb6wAC8sQsjPR5AFQEYWRSSdFlmnQ9BoEVyusx5wBYUGjiDLslz5sNDE7XaTk5NTZlt0dHSZbbmnDuG0ZaI1Ws47XnC70Z48jf7ocfRHj6M7ehzdsePoTp/xt76pArIg+EW4sViEGy786DMY0EVGUCgKxdtK7S85Vu8fJxkNdb4CGGGxYMvLq9P3rCoNxjafD8HtQXS7Edxu/6PLfXaby1W87/wxZ8e6Ed2es/vcbsTi7aX3SSqRoqVL8CS3qdCk+uTRuFypbJ4bNWoU7qIC3p4/G5W+5iqBC243lp1fEr1+M8bfDuAzGLANGkDOqGG4mpefI3iha0KSJQ4WHAmI7n15v+KTJcwqI92irg7kdzeq5faLDWYuuVhkGcHjQfB6AAEEAZ/JeDa/W3PhwmqRkZFYrdbate0i0Jg0GDwGjJqKv/eX+zz33nvvccsttzB//vzzCmo9/PDDQbKqLFWa5wrzWLJwHoIq+ItzNXa9+nxEfLKTuCXL0Z1Jo6htG9LvuoPCLldXeRHsaOFJtmd8zvb0XRwtOolKUNE96moGxffj+rhrCVOXv0BaJxQLcMHrJcJsJj8vz58CIwjF4ef+NoZlQtDrmFCd4wA0Zg0mjwm9uuKUtst9ngtVgj9b1TFikYOYlWuxfPZFGUEtq0RcjRNxNW9G/nV9cCY34/FV63CIIvMefxDR4UB0OP2PRaWeOxyoHM7ibWe3q/IL0GRklhrnRPR4OF/uXxhJozlfsJcS8z6DAdmgxxcYU9bbXnpcyXGy7jL3vhdX7xVKxKzLjeB2FT+eFbfnCVy3G9HlueA+nSxjKSxEKBG/rrLit0Q4C243orcaxaouZH7JqrBWG3iUtVqk4srDkl6PLzwcSafFbdIjmJT2SwoXj6zVYhvYH9vA/hh+P0j0+s1EfrCd6I1bsHfuSM6o4eT37FalmyJRELkivCVXhLdkavKtFHgL+Tb3R7/wzt7Lp1l7AGhhahrI7e4U0R6dSlvbH/PyQhAC8wbgz+92uPz5l7IMarVfeBsMSmG1BkRCgj+PtyRyRyGEUKmwDRqAbUA/Ird9Qtw7K0h5aDr2jh3IuGsiRVe1r/QUyaYm3JMygf9Lvo1D9iPstH3DpuMf8eRvL/Hs7xp6x1zDoPi+9IntjlEVhPojpT3gJhO+knshSULw+fz1JXw+/9ofQvG9js5fi0Kr9R+nVikecIV6SYW/oocPH6ZFixYAnDx5EofDQWpqap0YVuP4fERu+4T4Rf9Fk5NLQbcufkHdvCmu5Ga4miadvfkoJjHTHzrqbFkzP04RRiP56RmoHGWFulhU/NzpLPNadQFxr8nLLyvoHc4qv39F3ndVeDgmjeocb7u+XO+7r9T2at2MSVJZAVpazJ4rbosFqklUoc7LO39sac+u60Ie3tKe4mKhfIkBHJJWU0rcahH0egS1KiB+feFhgX2STnNWDOu0ZY4rEcf+fTpkneacfeeL6dIVSivDbbfSJCLikj6rQv1gwIAB2LNO1ep7OK5ozal/PEbafXcRtWUbUZs+oNmMZ3DHx5EzcgjWIYPwRVR1GRHC1CYGxPViQFwvZFnmSOGJgLd71cktLD2xAb2o45rIqwLe7qaGRKWFWE0jimULq/l8iPZChLz84sJqWiSzCUFvAElS8rvrKb179wYgOTmZn376iYkTJ/LII48wefLkIFtWdQYMGEBB2rFgm1F7qNVYhw7GNrA/UVu2Ebt0NS3ue5SCrp3JmDoRR9uKo9PA34GidVgLuiV14q9J4/k5/yDb03fxUcbnfJb1FXpRR7/YHgyO70vPmC5oxSAvZIoisiiCRlM2BF2S/E4RpwtBlqBkr0rtn5MMev8xKhWyRq3MSwohTbmh4x999BELFixg3bp1hIWF8cMPPzB9+nQee+wxBgwYUNd2XpCqho5rdn5Gk0XLMfx5hML2bUmb9lcc7SqftGqaWgkNlCQEl7tYlJ/vbRdLedtVDqdfzJ/jfRcdDjQuN7K90D+uyOFfXayqCVqNX3Tr9YEQeMHjOStuSwlj0eO5pI8rF1fcLSNKS732V8Isb5+2rFDW6fyvded4hgP7Sr/WXrCQUKiGorrtVpq06YhPG17hOCXUyI/dbmfRokVkZWXRr18/WrduTbNmzYJtFlC1eS7z0PfIXm+Nho5XiNdH+J6viV6/GfMP+5G0GmwDriNn1DD0Xbtc0jVR5HPyvfWngPA+XnQagCRDo4C3u2vkVRjVhmqfO1SvVwhR27w+BI+HCLOJvIJ8/xxvNoVMfrcSOl49Ro8ezdy5c2nZsiUnT57kiSeeYMWKFcE2C6jaPJf+81eo9IaGFTpeDoLTSfTGD4hdsRa1LY/8nt3JmDoRZ6sW1bbNJ/v40fYr29J38Unml9g8+YSpTfSP7cmghH50jeyIWqwbb/El/d2KBTg+qViAA8h+Aa7TIel1fgFe4gGvhgBXQscVaotyZ6slS5awZs0awsLCAOjUqRMrV67knnvuCRmhXRlCfj5NHplB+Jdf4W4Uz4lnZpB3fZ+g3xzUKKKIbNDjNeiByIs+zbmTn+DxnPWuOy/gfS8W6qoLiXuXG0mjvrC4PddTqyslZku8u+eMD4uJxuZ2+b26SvsIhVpgxowZ9OnTh++++46YmBhmzpzJ8uXLyx0vSRJPP/00Bw8eRKvV8txzz5UR5p999hmvvfYaarWaUaNGMWbMGDweD0888QSnT59GFEVmz54diBiqd6hV5PftRX7fXuiOHCN6w2Yit+8g6sOPcV3VHnn4TeRf1/ts7+dqYFTp6R3Tld4xXQE4WXSGPTnfsydnL5vTPmHNqS2oBTWdIq4MeLtbmZor3u7aoKSwmtmM5POLbnV2rj/MXBD8hdWMhkCFYWVuDm3UanWgjWGTJk0QFU9gyCLr9WSPG03u8JuIXvc+sSvX0WrSveT1603GlNtxJVd9IVglqOgS2YEukR14ovW9/M+6j23pu9iRuZtNaR8TqbFwQ3xvBsX3o1NEO0QhRL8XohiIPC3jIfT5ENxuVA7nWQEuy/6q5zo9kr7YUaKqvgBXULhUyhXaWq2WiHNCT6Ojo9GVtBCpB6h//x3DwT84NWU8tvHjkHXVC5OZPO0xAJa8Oq82zAtpZI0Gn0aDL7xij2hdIFksSKHm6VFoUNhsNkaPHs3mzZvp1KkTldWI3LFjB263mzVr1rBv3z7mzp3LG2+8AYDH4+GFF15g3bp1GAwGxo0bx3XXXcf+/fvxer2sXr2aPXv28PLLL7Nw4cJLtr10MbRg4EppzplHHyD9r5OJ3PYxsZu20vTZF/G8+ha5w28id/hf8MZcfNuZJsZEbjUmcmuTobglNz/afgt4u//159v868+3idNFB7zd3aOuJlwTVoOfUKEEWaM5u3giywhuN+qiQv9dryj687tNJiW/O0RJTExkwYIFdOzYkZ9++om4uNotPliTlC6GdjkhGY1kTRxHzsih/taLazYS/vlubDdcR+akCbibNK7W+TSiOjBXuto8wJ6cvWzP2MXmM5+w9tQHxOmiGRjfl0HxfbkyvHX9WMAsbkEGFxDgLhfqwiL/nuLf9bM9wHXFxdfUgX0KCjVNub+EgiDgdDrR68+GKjgcDjyXGPpbl3i6dePQB+/5q45XU2QrKChcXhw+fBiA9PT0Sj0933//fSDvsWPHjvzyyy9lztO0aVMsFn/OcufOndm7dy+pqan4fD4kScJut6NuYEJECjOTM+ZmfFPuwPvJZ0Sv30zcuyuJW7qavH69yLl5GEUd2l2S11MraukW1ZFuUR15uNVU0p1ZfJ3zA3tyvmNH5m42nvkIEZEOljaBm8m24a1C10NTn7lgYTWnv68uIGnUSCaTvxBnSTSSQlB54YUXWLVqFV988QUtWrTg3nvvDbZJClVECjOTOWUiOaOGE7vyPaLXbybi011YB99I5p3j8STEV/ucOpWW6+Ou5fq4aynyOvg8+1u2Z+xi9cktLDuxgcaGBPrH9qRteCtam1NoZkyqsxDzGqGkB/i5t/8+X3ERyCKQJRAE1HY7miKHP8pSr/MvKpb0AK8Piw0KIUu5d3oTJ07krrvu4o477qBJkyakp6fz9ttvM2HChLq0T0FBQaHWmTlzJjNmzODw4cM88MADPPXUUxWOt9vtmM3mwGuVSoXX60WtVmO32wMpNwAmkwm73Y7RaOT06dMMHjwYq9XKf/7znyrZplKpiI4u6xFWq9WBbRqNBkmtJiwiHK0uyG1cAJVaBQMHkDdwAIUnTmJes4HwjVuI+PRz3G1SsY8bTdHgG5ENl179NsJioU18SyYxBq/k5cfcX/k84xt2ZXzDa0eW8tqRpURpI+gT35V+CT24znQtkZaqF22rS1RqFRENybbidoZykRMcTtDqkMLMoNf787trIHzTKTmJCovCpA3+974+oFarMZlMREVFkZqait1uJyoqKthmKVQDX4SF9Hunkj32ZmKXryFq01Yitu/AOnQwmRNvxRsbc1HnNaoNDE7ox+CEfuR77OzM+optGbtYefJ9vLK/SrhO1NLS3JxUcwqtzSmkhqXQOiwl+O3DqkuJAC+1STaakB2us10YIJAiE0hvVAS4wkVQrtAeMGAAUVFRvPfee2RmZtK4cWMeeeQROnbsWJf2KSgoKNQ6rVu3Zs2aNVUebzabKSwsDLyWJCngoT53X2FhIWFhYbz77rv06tWLRx55hLS0NO644w62bNlSaTqOz+ersEiQx+PB6/VSYMtHpb+0FnI1QZl6D5Zwsu++E+H2W4n4+DNi1r9P1FNzCH/pFaxDBpEzciiexIQae+9Wmua0SmrO1KRbyXHb+CbnB/bk7OXz9G/ZdPJjANqGtQrkdncIvyJkPDQhWQytmBqxzeFEzMpGkCVk8BfQNBsvqbCaxqQhNzcXp6bi7htKkSA/s2bNIi4ujq+++oorr7ySxx9/nEWLFgXbrGogKCG+xXijo0j72z1kjRtN3NJVRG3+kMit28kdORTXPVOr1aXkXMI1ZoYn3sjwxBvxSB6OFJ7gYMERDtqPcLDgCDuzvmLjme2B8Yn6eFqHpZBqTqFNWAtam1NINMTXv0iikpoUlHKByzL4iqN17IUIsoxcPFedDUHXl+0BrghwhVJUGLvYqVMnOnXqVFe2KCgoKASF3r17k5ubS2RkJDabDa1WS0xMDE899RQ9e/Y8b3ynTp3YuXMnN910E/v27SvT9rBFixYcP34cm82G0Whk7969TJkyhcOHD6Mpzm+1WCx4vV581ajuX5+RDXqsw2/COmwwpn0/E71+sz/fcPV6Cq7tRs6oYdi7XF2jRWqitRH8pdH1/KXR9UiyxIGCw3xf+DM7Tu9myfE1LDq2ijC1iW5RVxeHmXcmQV9/clbrHWo1Ukm6hCwjeL3+wmrFSCYTksnoz+++iCJ6CpVz4sQJnn/+efbu3cv111/PW2+9FWyTqoUsigheH6iV70cJ3rhYzjz6AFnjxxD37nKi39uEvPlDNKOGkz1+9CXX2dGIGlqHtaB12NnCnbIsk+nK4aD9MAcLjnCoWIDvyvoGudhPbFIZSTUn+wV4mN8D3tLcnAhCM2qnXAShlAAvhSz7Q9CLHP50mdICXFfSttVfJDLgAVe4LCn3P9+rV6/Ac0EQ8Pl8tGjRgtmzZ9O8efMKT1pZRd5NmzaxePFiwsLCGDlyJLfccktg3/79+3nppZdYtmzZJXysmuHG6/sE2wQFBYU64JprrmHatGmkpKRw4sQJXn31Ve677z4ee+yxCwrtG264gT179nDrrbciyzJz5sxhy5YtFBUVMXbsWJ544gmmTJmCLMuMGjWK+Ph47rzzTmbMmMH48ePxeDw89NBDGI2X3o5r6NChFGSeuOTz1AmCQOHVHSi8ugPqzCyiN20lavOHhO/5BmfTJHJvHoZ18AAkU82GIoqCSNvwVlzbpAu3N7qZfI+db3N/DBRV25G5G4AWpmb0jO5Cr+gudIq8Mvh9ZhsqglC2sJokIThdqAsL/TewajU+owHZaFQKq9UgPp+P3NxcBEHAbrfXq6rjQ4cOxZqXDt7gR+2EIp7EBE7PeJSsCWNJWraW2BVrid64hewxN5M99mYkc83NqYIgEK+PIV4fQ5+YboHtRT4nf9qPcSjg/T7M5rQdFJ1yACAikhLWhJbG4vDzsBa0DkshVhtVP4qulUYQQK32txIrvb1EgBcWIeTnI8gCICOLoj//u0SAl/aAKzRoyu2jfSH27t3La6+9xjvvvFPhuI8//pjPPvuMuXPnsm/fPt58881ARd7c3FxGjRrFxo0bCQ8P584772TOnDkkJSWxaNEiNm/ejMFgYO3atZXaU9U+2k5bJlpj8FfRGnxoYC2h2FZ9lD7a1WP8+PGsXLky8Pr2229n2bJljBs3jlWrVgXRshDto10B1b0mBLcby84viV73PsbfD+IzGLANHkDOzcNwNW9a67bJsszhwuMB0f299Rc8sge9qKNr1FWBompNjdWr7lsTtoUKdW6bz4fg9SL4fMWF1TR+b3dJYbVigaj00a4e3333Hf/4xz/IysqiUaNGzJw5k2uvvTbYZgFVm+dOZP2JMS0TzMHvKhDq16vjx/3EL16K5fM9eMPMZI8fQ/bo4TVSG6M6SLLEaUd6QHgfcZ7kV+shzjgzAmMiNRZSw5JpbfYL79bmFJJNTdGIdbfAVuv/z2IBLni9CJIPQca/4CgIZyuga7UXFOBKH+36TbW+xV26dKlS1fGKKvKeOnWKNm3aBFqHtW/fnv3795OUlETTpk1ZuHAhf//736tjVq3hcPrzvgz6up2YFBQU6pbY2Fheeuklrr76an788UdiYmLYs2dPINQ7lCkqKsLhdKKvp14/WavFNrA/toH9Mfx2gOgNW4jcsp3oDVuwd+5I9ujhFFzbrdZW/gVBoKW5OS3Nzbmj2WiKfE72WvezJ3svu3O+44vs/wHQxNAoILqvieqIUaX8LtQa5xYr8voQ8wpQW23IAsg6HT6zCUFlBlH5P1SVtLQ0Pvroo0CaTGVexMqiEz/44AP++9//olKpSE1N5emnnwao8JiLpaioCIfXi1EQA0WqFMrHldKcE8/PQn/wD+IXLyXhzSVEr91A1oSx5I74C3IdteoVBZEmxkSaGBMZENcrIGjzPQUcsh8tE3q++tRm3JJfY6gFNS1NzQJh5yWPEZU4D0KW8jzgkuRfUMwvQPD5QABB9qdJSHo9sk6LIJpAZQiW5QqXSLXvzOx2e5XGlFeRt1mzZvz5559kZ2djMpn4+uuvA6HoAwcO5NSpU1W2pbJqvAByYSQOyY3WVP2L8+4HnwBg7TtvVPvYC9HgqsrWEYpt1ccp+lCr1EREX3z/4suJf/7zn6xZs4YvvviC1NRU7r//fn777TcWLFgQbNMq5fbbbw9qH+2axNG2DafatiHtvruI2rKNqE0f0Hz6M7gT4skZ8ResQwfjs9TujZZRpadPTLdASOSJotMBb/emMx+z+tQWNIKGjhFX0MqcTLKpCSnGpqSYmhKljah/IZD1geIcyZKKBoLHgzrHhsaejxCnBaXqeJVYu3Ytw4YNq3Kl8R07duB2u1mzZg379u1j7ty5gehEp9PJyy+/zJYtWzAYDDz88MPs3LkTn89X7jGXwu23347T6+SdBXNQeTxn28opVIizdSuO/3M2xl9+I+7tpSQufJPYVevInDgO69BBZ9M36phwTRhdIjvQJbJDYJtX8nGs6CQH7Uf84ecFR9iTs5fNaZ8ExsTrYsoUXks1p9DE2AiVUE9DsEURWRRBozlfgHu9CE4XGkcBQqNwUL7y9ZJyhfbu3bvLvHa73Xz88cd07ty50pNWVJHXYrEwffp07r//fhISEmjXrh2RkZEXZXxl1XgBrFYrznwbWm/1K1V6vf6f9ZoKJwn1UCPFtuoTqra57flYfN7zro9zUUKN/KhUKtq3b88VV1yBLMt88sknDBkyJNhmXbb4IiPImjiOrPFjCN/9FdHrN9PoP0uIX7Ic24B+5IwahrN1qzqxpamxMU2NjRnXZDgun5sfbb+wO2cvP9h+ZtOZjynyOQJjw9VmUkxNSTY1JcXUJPA8UR9X/yrwhjCB/G7ZHWxT6hVut5sRI0aQnJwcyM+eP39+ueMrik7UarWsXr0ag8HvafN6veh0Or788styj6kJJKMedY5NEdrVpOjKthx7eS6mH/YT//Z/abzgVWJXvkfmnbdhHTTgkqqU1xRqURWILvpLwvWB7TkuKwftRzhQcDjg/d6TsxefLAGgF3W0MifTplThtVRzCkZ1PfYClxHgyjxXnylXaG/durXMa71eT2pqKg6Ho5wjzlJRRV6v18v+/ftZsWIFXq+XSZMm8dBDD13CR1BQUFC4NKZNm4bH4yEzMxOfz0dcXJwitEMBtYr8fr3J79cb3ZFjRK9/n8iPPiXqw48pbN+WnJuHkd+vV515ZXQqLd2jO9E92t+NQ5ZlMlxZHCk8yZHCExwtPMGRwpPsyvq6TPsbvaijuTHJ7/0uJcSbGRujEUM/PUGhYfDoo49Wa3xF0YmiKBIT4+/ZvGzZMoqKiujZsyfbtm0r95iawB/yrLT4ulgKO13FkdfmY/7uB+IXvUvS3AXELl9N5uTbsfXvG5LFuaJ1kVyr68y10WcdfS6fm8OFxwNtxw4VHGF7xue8d/pDAAQEmhgaBYR3SeG1BF2sEnWkUKeUO/O98MILgec//fQTy5cvZ/HixQwcOLDSk1ZWkVej0XDzzTej0+mYNGlSlcOYFBQUFGoDu93O8uXLmTlzJk8++SSTJk0KtkkK5+BKac6Zx/5G+v9NIXLbx0Rv2ELTZ+bieTWK3GE3kTv8JrwxdZsqIQgCCfo4EvRxZW4CAWzufI4UnSgW4H4hvj/vd7Zl7AqMUQkiSYZEUkxNuCKqFYmquGIh3gSTOviF7RQaBllZWSxZsgSj0ciUKVOq3O2goujEktfz5s3j6NGjLFy4EEEQKj2mPCpLBdRoNHjxEhEXh7bQgWwwBjVPO1TTxqCKtt1wPTkDrqPw891YXn2LJs++SMKKteTdexeOAf1qtNVitW2rIvFRsVxLl8BrWZY5XZTOb3l/8Hven/xmO8RveX8GukoAWDThXGFpQduIVNpaWnGFpSWtwpND+v/pzrcSFR2F3hwRbFMULoJyZz+3283WrVtZuXIlGo0Gu93Op59+ir4KhcFEUeTZZ58ts61Fi7M9+KZNm8a0adMueGxSUlKVKo4rKCgo1BSq4lV8h8OBXq+vUtFHheAghZnJGXMzOaNHYP72e6I3bCb+neXELV1FXr9e5IwaTlH7tkEvlhShDaeT9ko6RVxZZnuRz8mxwpMB8X2k8ARHi07yZfb/8Mpn+6rH62IuGIYepbEoHhmFavH4449zww03kJeXx7x583jqqaeqdFxF0YkAs2bNQqvV8vrrrwdC0Ss7pjwqSwX0eDx4vV6sNhtalwuhyBG0/GII3bQxqKZtV3cgfdErhO/aTfySZcQ8MgNHqxZkTJlIQc9uNT6P1vbfzYyRrqar6Gq6ChL92wq9RfxhP8qBUoXXVh7ZhFNyAf4Fz5ZhybQ0NituO+b3gkfrLi6ttabR4iU3Jxedy1fhOCUVMDQpV2hff/31DBkyhHnz5tG8eXOmTp1aJZHdkBh+0w3BNkFBQaEOuPHGG3nttddo06YNY8aMKRP6GOqMGTOG/PRjwTaj7hFF7D2uwd7jGrSnThO98QMit35ExKef42jVgpxRw7AN6IccYr9bRpWetuGtaBteNsfcFGbil/TfS4Wh+x83njZnktQAACAASURBVNmOw+cMjLNowkgxNj0vDL2RkgeuUA5er5dx48YBcOedd1b5uIqiE6+88krWrVtHly5duOOOOwCYOHHiBY+pCcaMGUOOwy+6JaMRda4tqEK7QSGK5F/fh/y+PYnYsYu4Jcto/sRTFF3RmsxJt1HQtXO97mVvUhvpGNGOjhHtAtt8so8TRWcCwvuI8yTfWX9ia/pngTEx2qjitmPFoefmFJoZk1CLoRderxC6lHvlTJw4kQ8++IDTp08zevRoqtFuu8Ew/KYbg22CgoJCHdCiRQu6deuGIAj07du3RtrR1BVjx44N9NG+XHEnNSbt/r+SPvUOIj7+jJj175M0918kvP421iGDyBkxBE9iQrDNrBCNqCa5WDj3p2dguyRLZLiyz3q/iwX4zqyv2XBuHripid/7XUqINzUmKnnglzmlIyAkSarycZVFJx44cOCCx517TE0wduxYjucdB/C3PLoM70lrHZXK32qxf18it+8g7p0VNP/7LLzhYeT36k5+n17Yr+mErKv/hehUgopkUxOSTU0YGN834Gm3ufMDOd8H7Ic5VHCEZbkb8cr+31edqKWFqVlAeKeG+T3gYWql84HChSlXaN99993cfffd/O9//+O9997jl19+Yd68eQwfPrzKoUD1HavNH94SGRGaeRsKCgo1w8KFC+nevTsArVu3DrI11SMnJwerLY8Is/JDLxv0WIffhHXYYEz7fiZ6/WZi1qwnZtU6Cq7thnPiOGjbOuhh5dVBFEQa6eNopI+jZ3SXMvus7rwy4vtI0Ql+tP3Kh+k7A2PUgookQ6NA7neKyd+KLNnYpH5X5VWoMg6Hg2PHjiFJEk6nk2PHjgWcJ8nJyUG2rmrk5ORgK7ARGxOLrNUiC4rQrjXUaqxDBmG78XrCvtlL+Bd7sHzxNVEffoLPYKCgxzXk9+1JQY+uSFXM968vRGjD6RbVkW5RHQPbPJKHI4Unzvb8th9hZ9ZXZQpeJurjA23HWoel0MbcgkRDvBJlpIAgV9FVnZ+fz/vvv8/69evZtGlTbdtVJdxud6XtvXJPHcJpy0RrrL5YnjztMQCWvDrv0gwtpsHk9NQxim3Vx2230qRNR3zainsOKzk9fiZMmIDFYinT9ubhhx8OslV+KpvnRo0aFeijrdIH/6Yn1K4JTUYmUe9/SNTmD1Hb8nA2TSL35mFYBw9AMoXO4kRN/t2KvA6OFZ06zwt+0nGmTB54gi62jAAvCUOP0pYtuhNq/9MSdLIbdUwzDJaYCsdd7vPc7bfffsHtgiCwdOnSOrbmwlRlnnN6nSxasQhRENGcPoOMELSQ5lC9JqCWbPN6Mf+4n/DP9xD+xVdocq1IWg32Lp3I79uT/F498Fkqvt+oNdtqgOraJcsyma6cQOj5QfthDhYc4XjRaeTiqvgmlZFUc3LA693anEJLc3MMquqlM2lxo4tric5csY653Oe5UKXKM1R4eDi33357uRO2goKCQn1l1KhRwTZBoZbwxMeRcfedZN4xnkbf7sWwbBWJL79O/JvvYBs8gJybh+Fq3jTYZtYoRrXhgnngHsnLSceZ8wT4D6d/CRQGAojQhJ/1fhub0j6uDXFEk6CPVTw09ZBly5YF24QaRzIaEfMKkOtx7nC9Qq3Gfk1n7Nd05sxD92H85XcsX+zxC++vvkVWiRR27EBe317k97m2zjtA1DWCIBCvjyFeH0PvmK6B7Q6fkz/tx8q0Hfsg7VPWnNoCgIhIU2MibcJaBLzfqeYU4nTRSpHLBooyQykoKFz2DB06lI0bN5KWlka3bt1o1apV5Qcp1CtknZaioYM50+daDL8dIHr9ZiK3bCd6wxYKulxNzqhhFFzbLST7yNYUGlEdCB0vjSRLpDuzzorv4rZkn2buYb1nG/zhH6cXdaW832fD0JsYEtGIyu2EQt0h6XWorDalo3Yw+H/27jxOqvJK/P/nLrV2VfXK0uzQLCZuBDRRATEqToJ5xQhqAxk0jhPjfGPMyzGOxnkFlyhqYiYuMSqZqJFoxBgTxURjcIHQmow4ovLLgIqKiKy911733uf3x+0quuimu+mtejnvpF/dfW9VccCup++5z/OcYxjEjz+G+PHHsPvyS/G/+z7F62uIbKhh7H/9jLH/9TNix3yGplPn0DR/DumxYwodcb8JGH6OLT6KY4uPyh1zlMOuxB62tcx+vxv9gLcbt/L83vW5x5R6itsUXptcNF7qawwB8ptRCDHsXX/99YwcOZJXX32VY445hmuuuYZf/OIXhQ5L9JHEZ4/ik88exe7LL6XsmT9R/oc/Mun7N5IePYrac79C/Ve+1KVlkEOFrumMCYxiTGAUcytOzDtXl25gP3W8ve//con4G/Xv5FXnNTWD8YExbRLwSUXjCR7hMkkhukJ5PDIDOBBoGskZ00jOmMbeS7+B76OPibTMdFf+/L+p/Pl/k5g6hab5c2icPxdmHlfoiPudrumMD45hfHAMZ46cmzvelInybvRD3m219PzxT9aSdtz2oqZmUlU0gfkVs7l2xI2FCl/0kCTaQohh7+OPP+aWW25h06ZNnH766axatarQIYl+YJeWsP+iZez/ejWRja9S/uQzVN73S0b9cjUNZ55G7XnnkJw+tdBhFlSZt4QpxROZ4Z2SdzxuJfgwvrPNMvT1B/6Wtw+80j/STb6DE/IS8VKvFBkVPWCaOKYBtj2kV6EMNqlJE9g/aQL7L1yKZ/ceIn99leJXNjLywV8z6peryUwcj2/uyTTNn0PiM4OrMGVvi3hCnFB6LCeUHps7Zjk2O+Kf5Cqeb4t+wPbYx3JTaRCTRLsDF5x7dqFDEEL0A9u2qaurQ9M0otForiDaYHDhhRfStPvDQocxuJkGTafNo+m0efi2f0j5U89Q+ucXKfvTC8SO/Sy1i75K02lzpW9vK0EzwNGR6Rwdye9CknEy7EzsdhPw6Mcty9B38r/1f8rbB17qKW53Gfpo3wi5qOxlc+e6s2iZTIZEIkFlZSV79uyhvLycl156qZNnDwwXXnghB+IH8o45wSB6LI6SRHtAylSOpvaCRdResAizto7IX1+j7NW/M+Lx3zHy0SdIj6xwl5efOofYcceAKf8dTd2gKjSRqtBEzh59OuAWQxODlyTaHfjSGacVOgQhRD+48sorWbp0Kfv376e6upr//M//LHRIXXbOOecM+z7avSlVNZlPr/4ue771L5Q+9xfKn3qGCTfeRuZnZdR9dSF15ywc8oV+esKjew7uAx958LijHHYn9/FBbCcfxj7OzYav27eRhkxT7nEBw8/k4KEJ+HjGyT7wbtu4cSMA3/ve97jqqquorKxk79693HrrrQWOrOvOOeecXB/tLBUIoDU2oXy+AkUlusoqL6Pua2fjXLSMpp2fEHn170TW11D2zHNUPPk0VkkxTXNPpnH+XGKzj0d5B3+vbiFAEu0O7dm7D4DRo0Z28kghxGAWDof585//TF1dHaWlpYNqRm3Xrl3U7tvPqLLSQocypDiRMLXVi6g9/2uE/76J8t89w6iHfs3IR35D42nzqDtnIfGjP4PyyQVhV+iaztjAaMYGRjOvnX3g2QQ8uxT99fq3eHbPi7nHmJrBhODYXBI+pWgCn4tMZhoT+/uvMmh98sknuRZAo0aNYvfu3QWOqOt27drF3ua9jBs3LnfM8cgl7GDkRMI0fOlMGr50JloiSfjvr1O8vobilzdQ9uzz2EVBmk/5Ao3z59D8hRNRAanzIAYvGaU6cN0P3f7ZvdVHWwgxMN155500NDSwaNEizj77bIoGUH/lzlxxxRW5PtqiD+g6zSd/nuaTP4935y7Kf7+W0j+9QMmLr6AMg+TkiSRmTMt9JKdOlhm2I1TmLaHMW5K3VxEgZsX5MLYzN/v9YWwn22M7eOXAa9jKwdB0Xh+9gQAd99EWrqqqKq6++mqOO+44Nm/ezOzZswsdUpddccUVuT7aOR6Puz/bcWAQbfcRB6mAP7dtR0unKXrjLYo3bCTy19co+cvLOF4vzV84we3VfcoXcCLhQocsxBGRRFsIMezdf//97N+/n6effppLLrmEqqoqbrnllkKHJQaY9Pix7L7iMvb+60WE/mcTgW3vEdj2PpGNr1H2xz8DoAyd5KSJJGZMJTljGonpU0lMq0L5ZVbmSBWZQY4pnsExxTPyjmecDB/HP8WxY0Q8cuHdVVdffTWbN2/mvffeY+HChZxxxhmFDqnH7GAAPZGSlSVDgPJ6iZ58ItGTT2TXVVdQ9PYWIhtq3Nnuv76KMgyis2e6+7rnnYxVXlbokIXolCTaQggBWJZFOp3GcRwMKa4jOuAEA7lZGACUwrN3f0vi/R6Bd98n/NrrlP3pL+5pXSc1cbybdGdnvqdV4QQDBfxbDF4e3UNVaCI+lR5U2zwK7d/+7d/4zW9+w2mnnVboUHqNCgTQolEUkmgPKaZBbNbxxGYdz+4rLiOw9V0i62soXr+RsXfczZif3EP82M/SOH8uTaeeQqZydKEjFqJdkmgLIYa9iy66iFQqxXnnncfdd9/NM888U+iQxGCiaWRGjyQzeiRN8+e4x5TC3H8gl3gHtr1HaNOblP7Z3XesNI3UhHG5xNucNRN9zCicQbRtQQwuxcXF/OpXv2Ly5Mm5zgrZiuSDlfJ6QRU6CtGndJ3EZ48i8dmj2HvZv+D7cAfF6zcSWV/DmHseYMw9D5CYPpXG0+bSNH8OqYkTCh2xEDmSaAshhr3rrruOVCrFr3/9a+68807OOuusQockBjtNwxo5guaRI2ied0rusHmgNrfkPLDtPUL/+xalL7gtlkYCqfHjSMyY6s5+HzWNxPRpOCFJvkXPlZaWsnXrVrZu3Zo7NugTbY+JHSrCiMZwgsFh3Zd5WNA0UlMmsW/KJPZd/M94d31KZH0NkQ01jF71MKNXPUxy0gSaTp1D4/w5JKdPlZ8JUVCSaHfgwiWLCx2CEKIPpdNp/vjHP/Loo4/i9XqJRqOsW7cO/yDaT/utb32Lxk+3FzoM0UVWRTnNFeU0zzkpd8ysraPik904b27Gv+19gm//f5SseyV3PjVuTN6y88T0qVIUSByxQ9t57du3r0CRHLlvfetb7Iu3E6+mYY+oAMPAbGzEDgSlMNowkh47hgPLzufAsvMx9x8gsuFVijfUMOLRNYx85DekR4+iaf4cGk+dQ/yYz7jF84ToR5Jod+C0uSd1/iAhxKB1+umn85WvfIU77riDSZMm8a//+q+DKskGOOuss6SP9iBnlZeRnDKZhuOPzh0z6hvylp0H/7GNkpc25M6nK0cfTLyPmkpi+jTs4kghwheDxN13381jjz1GJpMhmUwyadIk/vjHPxY6rC4566yz2vTRztE07PIylGliHqjFCQQkoRqGrBEV1C3+KnWLv4rR0Eik5m9ur+6n1lKx5ikyZaU0zTuZpvlzic46HkxJgUTf65OfMsdxuOGGG9i2bRter5ebb76ZiRMP9rr8wx/+wC9/+UvC4TDnnnsu559/fqfPKYQPP94JwOQJ4wsahxCib1x44YU8++yz7Nq1i/POOw+lBt9mv/fff5+6jz9h4hgpBjOU2KUlRE86kehJB3tOG41NBN59j8BWN/n2v/s+xa/8NXc+PXrUwWXnLUm4XVpSiPDFALRhwwY2bNjAypUrufjii7nxxhsLHVKXvf/++3wa/ZSpU6ce9jFOcQTLMPDs24/t84MpyfZwZZcUU3/2P1F/9j+hx2KE//Y6kfU1lLzwEuVP/wk7FKJpzhdomj+X5s/PguLiQocshqg+SbTXrVtHOp1mzZo1bN68mdtuu4377rsPgLq6Ou666y5+//vfE4lE+MY3vsHJJ5/MP/7xj8M+p1cphZbJgO247SA6WGL0wx/dDUgfbSGGqksvvZRLL72U//mf/+G3v/0tW7Zs4cc//jHnnHMO06dPL3R4XXLNNddIH+1hwi6OED1xNtETD/Y/1puac7Pe2c/F62ty59MjRxxsNdbyYZWVFiJ8UWAlJSV4vV5isRgTJ04kkUgUOqQuu+aaa9r20W6HEyoibRp4du9BOR63WJoY1pyiIhrPOI3GM05DS6UIvf4mkZZe3aV/fhHH7yM57xTUKV+g+ZTPS0FK0av6JNF+4403mDfPbXsyc+ZMtmzZkjv3ySefcNRRR1FS4t5lP/bYY3nrrbd4++23D/ucntIzFno8DkqBpuEEAqigidHQKEuMhBB8/vOf5/Of/zxNTU08/fTT/Md//Ad/+MMfCh2WEJ1yImFiJ3yO2Amfyx3Tm6ME3ns/V3AtsO19iv/6Wu58pqI8b7934qhpWBXlhQhf9KPRo0fz5JNPEggE+MlPfkI0Gi10SH1C+f1kxozBs2cvpFIon6/QIYkBQvl8NM89iea5J7HLsih6822K19dQsvE1JvzlZRyPh+gJn6Np/hya5pwkK4JEj/VJoh2NRgmFQrnvDcPAsixM02TixIm8//77HDhwgKKiIl577TUmTZrU4XN6xOfDikTQwyUojwflMXMVCJXfj2fvPmyfT/ZqCCGIRCIsX76c5cuXFzoUIbrNCYeIzZpJbNbM3DE9FsP/3gf5vb5f/Ttay3aJTHlZm2Xn1ogKqdg7hNx0003s3r2bL33pS/z+97/npz/9aaFD6hWWY2Hq+ddwyuclPWY0nr370RMJd1JFiNZMk9iJs4idOIv4jdeRfvVvLb26a4i89j8oXSd2/DE0zZ9L46mnYI0cUeiIxSDUJ9llKBQiFovlvnccJ5cwFxcX8/3vf5/vfOc7jB49mqOPPprS0tIOn3M4hmFQXp5/F940zbxjTsDBSJXgN9spcFRailZejr57N5ge8HgOeS13prukl/ZuGKbRa6/V2yS27hmosSV1G9MwKSmXWSohhLt8Mj7zWOIzj80d0+MJ/O9tz1t2Hv7bJjTHASBTWkJy+lSc447BmTSBxIxpZEaNkOR7EFqzZg2LFy9m7NixbNq0CdM0O9zvPFgkrAS2sjEds+11nsdDpnIU5v5a9Hjcbf8lRHt0nfixRxM/9mj2fPub+N//gMj6jRSvr2HMnT9nzJ0/J/6ZGTTOn0PT/Lmkx48tdMRikOiTRHvWrFm8/PLLLFy4kM2bN+ftdbQsi7feeotHH30Uy7K4+OKLufLKK7Ft+7DPORzbtqmtrc07Vl5ennesLlFHNB1tP9FuoQWDePbsRWm6u287F6sNQENjY5f/7h0pKS7utdfqbRJb9wzU2NLRJoptq83741CVlZX9FJEQYqBxggHixx9D/Phjcse0RJLA+9sJvPs+/pZl5/4HV1Nsu78PrZLillnvlpnv6dPIVI6S5HsAu+eee3jvvff46le/immajB49mocffpi6ujq+/e1vFzq8bkvbaQzdYExwDHtie0jZKXzGIcvEDQNrZAVGbR1mcxSlablthAe5qzo01bLa8bA/ysp9qKahmSZ6POG+HkD2ObnXbf+46uh87rXkvVRQmkZyWhXJaVXs+9eL8H68k+L1rxLZsJHK+x+k8v4HSU6ZlEu6k1WT5b+ZOKw+SbQXLFhATU0NS5YsQSnFypUrWbt2LfF4nOrqajweD4sWLcLn83HxxRdTVlbW7nP6S+slRloyiWpp73PpRUv7LQYhhOiO7373uzR88l6hwxBDhAr4czM7WaU+H8n/fevgsvNt7zHisSfRssl3JExi+lSS2WXnR00jPaZSLj4HiA0bNvDEE0+gtfz3GDduHD/96U9ZsmTJoEm0v/vd77I3tjf3veVYZJwM48Pj8RpexoTG8Gnzp6TtNF7jkAJouo49ouLgfluVTazJ+z6bcOe+V6rtY1o+28URLK8JTstrtawC4ZDPuednjyvH3a6hHPePUwqt5TUOPveQeHI5udb6aDtaYjFNjFisw5sKrTJ892gHNwyUxxzWvcnTE8azf3k1+5dX49mzj8iGGiIbahj58GOMeuhRUmMrW5aXzyHx2RnD+t9KtNUnibau69x00015x6qqqnJfX3755Vx++eWdPqdftSwx8uzdjx6P4QSLOOnEWYWLRwghuuDUU09l37tF0kdb9Bnl95M4+igSRx+VO6al0vg/+DBXbC2w7T3Kn/g9esvPoR0qytvvnZg+lfS4MXIRWgDBYDCXZGd5PB6KBlF15VNPPTXXR9tRDgkrwdjw2FxS7dE9VIYq2dW8izTtJNvQphZPV5o5Hu4xKhLBabnR1GdU28S/vc+aIu9mgV1SSroo0PYx7b5GfnKvOY57rtWHEY0BCscfGPbv38zokdRecC61F5yLWVdPeONrFK+vofyJ3zPisd+SqSin6dRTaJw/l9jxx0qLOdE3ifagZRhkRo/E3H8APRbjH5/uAeCoaVWdPFEIIQpjy5Yt1O34gBmTJxQ6FDGMKJ+XxGdmkPjMjNwxLZ3G98EOt9d3SwJe/uTT6JkMAHZRkMS0qa3ajU0lNX7csL9472t+v5+dO3cyfvz43LGdO3e2Sb4Hsi1btrA7upvPHv1ZknaSEcERBM38Pddew8uY8Bh2Ne9CQ8NjeA7zaoNEF5eSH3ozQPl9uZWZh3vMkbDLStGjMcz6BlAKx+eTbj2AVVZK/VcXUv/VhejNUSKv/Q+R9Rsp/eMLlD+1FisSpmnuyTTNn0P0hFl5W1PF8CGJ9qF0HWvkCIwDtfz4v+5FGToP/uyOQkclhBDtuv7666WPthgQlNdL8qhpJI+aRn3LMS2TwffRx27ivdVNwMt/vxY93ZJ8BwIkp1flzX6nJoyTC/le9L3vfY//9//+HyeffDLjx4/n008/ZePGjdx+++2FDq3Lrr/+epJWkrt+dRfF3mKKve0XIfUZPsaGx/JJ8yeguTPdoocMA6c4QjocQo/F3YQ7mXQTbunYA7idHhrOOp2Gs05HSyYJ//0NIhtqKN5QQ9mfXsAOBGg+5fM0nTqH5pNPlMJ8w4i8Q9qjadgV5e6+FMtCSyRRXo/84u+EUg62Y2NhtRQMAb3lf5qmoWs6miYzF2LwcxyHG264gW3btuH1ern55puZOHFi7vxLL73Evffei2maLF68mAsuuACABx54gJdeeolMJsPSpUs5//zzC/VXEKJfKI8nV1io/itfcg9aFv6W5NvfUu287Jnn0FNu73rH7yMxrergsvMZ00hNGC/LMLtp2rRpPPbYY7z44ovs27ePo48+mm9/+9t5LVUHC5/hozxQ3uFsvM/wMTY0lk+in6ChtWn9JbpJ13HCIdKhIvRYHKOhAT0Ww/F6UR65oZGl/H63D/f8OWiZDEX/+xbFr2wk8tdXKXlxPY7XQ/TE2TTOn0Pz3JOwI5FChyz6kIw+h6NpKNMtAOFEQu4elWQSpRuSdCuFpSxsZeMoBw0NhULTNPyGn6AexNANbMfGVu5HtnCJrdw9TQqVK+yRfT6ApmloaAeTc/TcOSEGinXr1pFOp1mzZg2bN2/mtttu47777gMgk8lw66238uSTTxIIBFi6dClf/OIX+eCDD3jzzTf5zW9+QyKR4MEHHyzw30KIAjFNklOnkJw6Bc7+J/eYZeP7eGfenu+yZ59Hf/JpAByfj+TUKS29vt1l52qSdE3oqnA4zNe+9rVCh9Ejpm4yumg0ehdu2PtNP2NDY9kV3YUfvyTbvUnTcEJFOEVB9EQSo74BIxrD8XhkefQhlMdD9AsnEP3CCez63ncIvvMPitdvJLK+hkjN31CGTvRzx7uJ+bxTsCqkJetQIyNPZ3Qdu6wUu7QELZ1GjycwmqPDJul2HBtLWTgtBTMc5aBrOqZuUuQpwmf4MDUTQze6/IvMUQ6OclBKoVDu1y2fs0m57djuBzZJK0nCiue9Rl5yjtZm9lzDnUGXqreiL7zxxhvMmzcPgJkzZ7Jly5bcue3btzNhwgSKW/qrz549m02bNvGPf/yD6dOn8+1vf5toNMp//Md/9F5AmtazTXhCFJppkJoyidSUSTR8eYF7zLbx7dyVq3Tuf/d9Sp5bR/lTawGwSkvY+ec/QnFFAQMX/cXUzSNaCh4wA4wpGsOu6C6CpjsBIHqRpuEEAzjBAFoyiVHfiB6LoUwT5fN1/vzhxjCIzzyW+Mxj2X3FZQS2vef26n6lhrE/+Rlj/ute4sd8hqZT59B46hwyY+VG4lAgiXZXaRrK58P2+bBLitHSGfR4PJd0o+k4Pu+gTbodHCw7g4WVN0vt0T34Tb+bULf8kjM0o0dFVHRN79Id6azSklLqnLpcQq6Um5Q7uN/nkvJWs+fZ2Xal8rOPw86eo8nydtFl0Wg0b9mlYRhYloVpmkSjUcLhcO5cUVER0WiU+vp6Pv30U+6//34++eQT/u3f/o3nn3++0/eSYRiUl+ff5TZNM3fM4/HgOD6KA37M4vb3LfYnwzQoGQBxtEdi656CxlZWBscfSwJIAPWOg7ljJ95/bMXZv5eSceMoKpdZING+oCdIZVElu2O7JdnuQ8rvx6r0o6VS6I1NGNGYOxnl98mER3s0jcRR00kcNZ29l16M76OPiax393RX3vsLKu/9BYlpVTTNn0PsjDkwcmqhIxbdJIl2By6/6vL2T2gayufF9nkPJt2JBEZTM6RSgDZwk+6WZd+WsvKSUL/lwzRMiowivIY3N0t9JAlxX9I0DUM78n/PbFKenTHvaPbcwSHtpHOz97k/u53Z82yCnnEyOI4ts+fDTCgUIhaL5b53HAezpSjMoedisRjhcJiSkhKmTJmC1+tlypQp+Hw+6urq2iTRh7Jtm9ra2rxj5eXluWNXXXUV+2P7aYzHMSxV8H2sJcXFNDQ2FjSGw5HYumfAxVZWAnNPwqfSmI2NJOl4lrOyUmaGBrtrr722288NeUNUUsm++D6U7f4u9+rewV+VfABSPh/2yBE4JSXoTU2YzVGUpuH4/XKNdDiaRmryRPZPnsj+byzD8+keijfUEFm/kVH//QjW757hk9dqCh2l6CZJtDtw/KzjO39Q66S7OJKfdBd4pttumdm1lX1wjpoHFwAAIABJREFUHzUaHt1DyBPCb/oxcJd8V5RUUK/qO3/RQaY7CXrekvbDzJ47ysFSboJtY5N20h3PnrezvF1mzwevWbNm8fLLL7Nw4UI2b97M9OnTc+eqqqrYsWMHDQ0NBINBNm3axCWXXILP5+ORRx7h4osvZt++fSQSCUpKSnocy4knnsiu5l1YzU14ahtxzMHTG1cIIbrixBNP7NHzQ94QRZ4iUnaKtJ2mOd1MLOPeENU1HY/ukX3cvUh5PdgV5djFEYzmKEZjI6BBq9Veon2ZMaM5sGQxB5YsxjxQi7+pDk1aIA5aMqp04K3/fQvoYsINnSbdmscDtt3rSbdSjjszi52X7Jm6ScAM4DW8uV8iPV32PRzk7fHuRGm4lIAVADqfPc8m6O0Vh8v92Whuoq+p3HL2Q4vDyex54S1YsICamhqWLFmCUoqVK1eydu1a4vE41dXVXHvttVxyySUopVi8eDGjRo1i1KhRvP7665x33nkopVixYgVGL4wFr7/+Ovvj+zn2+KOhMQaWXfBZbSGE6E2vv/460LOEW9M0/KYfv+kn4ovgKIeUnSKRSRCzYrnE29AMd5ucLDPvOY/HrXMUCbtFhVMp9HhcenF3kVVRTroijOx4H7wk0e7Az37yMwB+8egvjvzJhy4vT6Wx/X60jz9Gy850e71HdkHcatl3633UpmbiNbyEzbCbUA+wZd/DRXeXt7cuDnfo7LlCtSkO197sORys5K6h4dhWb/yVxGHous5NN92Ud6yqqir39emnn87pp5/e5nm9WgCtxW233UbKTnHfI/dhlZZg7j8gs9pCiCHltttuA+B3v/tdr72mrukEzAABM0AZZViORdpOE8/EiWViJKwEmua2B/PqXpmk6AnTxC4pxo5EsEwds64BUikcr09uDIshTRLtfqJ8XlRJMRk1Di2Vdis0NjWhxdpPum3Hyqv2De5sp1f3EvaE8Zm+3LJvues6uB1pcTg4/Ox5NkF3bLeIXazzlxJDiFMUhDqjT1bOCCHEUGbqJqZuEvQEqaCCjJMhbaeJpqPEMrFcG1OP5mn3ZrfoAsPACYdJF7XuxZ3E9vnAlJREDD3yU10AyudF+bw4xRGcZBIVj0JDA8QyODrg8eHx+gmaQVn2LdrV6ey5bbqJtuMc/jFi6NF1rLISzAN1OMFgoaMRQohBy6N78OgeijxFKKVIO2lSVopoxk28Y5mYW/fG8BxR2zEB6DpOOIQTKnJ7cdfVo8daenF7pRe3GDok0e4nSinSdpqElcibpTZNE19ZBf6R4/Da4Emk8cWSkMmgKQ2le+UunxCiy5yiIqitl1ltIUSPOI7DDTfcwLZt2/B6vdx8881MnDgx7zGJRIKLL76YW265Jbd95mtf+1quxeG4ceO49dZb+z323qZpGj7Dh8/wEfFFKC0rZbe1m6SdpDndTDQTRVMauq7j1b2y0rCrsr24A360ZBKzvhE9GkV53AkpIQY7yeD6gOVYWI6FgwPK7VFtaAYlegnF3mJ8ptuTOruXOk8QnHIgnUaLx9GamtCiUdB19y6fJN1CiI5kZ7Vr60n53JUwulz0CSGO0Lp160in06xZs4bNmzdz2223cd999+XOv/POO1x//fXs3bs3dyyVSgGwevXqfo+3P+maniusVuIrwXZsUo5bWC2aiZLMJAG3sJrX8ErNnM5oGioQIBMIoKVSGPWN6LEYyjBQPunFLQYvydo68L3//F6H523HzlWQVkpByzjgNbyEPCF8htubOrv0uzxSTm2mtsPXzPF6UV4vqqTkYNLd3CxJtxAiz4033si++L68Y05REaq2HstOowwPltXqok/3Sls5IUSn3njjDebNmwfAzJkz2bJlS975dDrNvffem1fkcevWrSQSCf7lX/4Fy7L493//d2bOnNnjWG688cYev0ZfMnSDoB4kaAYpD5Tn9ndnl5hn26xml6PLNsDDUz4f1uiRaKl0rhe3oxsovyTcYvCRTK0DMz47A3CrQtuOnStOlq3ubGruPthiX3Fuj46pmb0/gB6adCeTaI2NB5Nujwc8sj9IiOHomGOOYVfzrrwtKRgGqeIQwcYmRpSMy7WTS2QSxK04trIPdizQvXLxIoRoIxqNEgqFct8bhoFlWZgtN/lnz57d5jl+v59LLrmE888/n48++ohvfvObPP/887nndNcxxxzTo+f3t0P3d2ecjLu/24oSz8QBZH93J5TPiz2iArukGKOpGaOpCdBw/H6QvtJikJBEuwM1G2vI2Bm+MOcL+AwfRUYRftOPobs9FguyFCibdEciknQLIdiwYQMHEgc48eT8/rKpIh+RWAAyGUyPu6om28YmY2fIqAyxtNvCRuFW0NU1HY/mkaXmQghCoRCx2MHeFY7jdJowT548mYkTJ6JpGpMnT6akpIT9+/dTWVnZ4fMMw6C8vDzvmGmauWMvvvgiAGeccUZ3/iq9rnVsR0oplevfHU1Hc2Nwdpm5qffs0tw0TUpLS3v0Gn2l27GNHAmWhd4cRauvR3MUyu/vtTokhmlQUlzcK6/V29JN9ZSVl+EPlRQ6FNENkmgfhlKKX/78l3gNL0sWLil0OO1rnXRnMmiJxMGkW9Pc5eWSdAsxpN11112k7FSbRNvRNfRxE9EONKJsG/z+3DmP4cGDh6AZzM22WMoikUmQsBJYVhIdHTQwNbeehMx6CzG8zJo1i5dffpmFCxeyefNmpk+f3ulznnzySd59911uuOEG9u7dSzQaZcSIEZ0+z7Ztamvzt9aVl5fnjv3whz8E6JVl6L2hdWw9ESSIz/GRdtIkMglqrVrSdhoNrdv7u0tLS6mvr+9xbH2hV2KLhNGjMcwDB8C2cXz+HvfiLikupqGxsWdx9REvFnW1dfhSdoeP6+xmligMSbQPI56J49E9HbdQGkg8HpTH0zbpbrkbLUn3MKCUW2laKTTLKnQ0osAUCjNQhD0ujL57N1oshioqavM4TdPwGl68eAmabkuw7FLzjJMhno6TsBO5LTOGZrTsMZSle0IMZQsWLKCmpoYlS5aglGLlypWsXbuWeDxOdXV1u88577zz+P73v8/SpUvRNI2VK1f2eNn4UGfoBgE9kFtxZDkWaTtNPBMnlolhKff3uambLTU2hvlNT8PAKY6QDofQY3HM+gZIJXGkF7cYgPrkJ7KzlhDPPPMMDz30ELqus3jxYpYtW0Y6neb73/8+O3fuJBQKsWLFCiZNmtQX4XUqaScJeoI9Xr5TMIdLumWme3BynFwCjW2jKeV+rWkopcj9ytV1lGm6H6WlEAxCNFrIyEWBuD8XbuEdNA1n7Fi0/fvRm5vdZLuTCzVTN92l5gSIeCM4ysld/CXtJEkriaUsNKWh6W69ClP2GQoxpOi6zk033ZR3LNvCq7XWFca9Xi8/+clP+jy2oSw7/gY9QSqoIG2n8wqrOTi5bT6m3gd1gQaLll7c6VARejyBUV+PHovieH3uNkohBoA+ySQ7awnxox/9iGeffZZgMMjZZ5/N2Wefzdq1awkGgzzxxBN88MEH/PCHP+SXv/xlX4TXoYyTAQUjgp0vdRoU2ku6sy3DJOkuHKXcBLrVh+a0FLNqnUBrGhiG2+KipdK88njcY7ruFgQxDPfzocVBfD5JtIcpS1l4jVYzH7qOGjkSx+NBq611b8Icwd42XdPdWW/DSwi3OFJ21jtlpYhbcRKWW+DHa3lI2ymZ9RZCiF6QG3u9IZRSpJ00KStFc7qZuBVHQ0PTtFyHm2FH03CKgjjBQEsv7ga3NZjpkV7couD65B3ZWUuIGTNm0NzcjGmabkKhabz//vuceuqpAEyZMoXt27f3RWgdcpRD0koyPjx+aA5WhybdrQupeTyQyUjS3VPtJc+Hm302DDdx9vvd/zam6R4/NIEernerRbdZjkXYG84/qGmosjLwetH37HF7k/bg/Z6b9TYDlFCCoxwydoZQIEQmliFhJ3AcB01ruQjUPBhDcVwVQoh+omkaPsOHz/AR8bmrjVJ2iqSVJJqJEs1EMdMmCSuBV/diDKfClq17cSeTB3txm6b7+06IAuiTq57OWkJMmzaNxYsXEwgEWLBgAZFIhM985jO8/PLLnHnmmbz11lvs3bsX27YxOph16axKJYATd/CkPPhN/6FPb6M53cy44DhKA25FxAceeACg25UlD9WTKpV9KpPBzGQora2FRMI95vW6HwNAwStotjP7jO0WpTCTSUpbJyuG4SYvpul+9nrdY9mk+XCzz31gwP68iV51++23sze2N++YoxwCZqDdx6tQCHvsWIw9e1CO46586AW6puMzfYR8odyKoOysd9JKkrASuVlvcIusZZe2CyFER26//fZChzAg6ZpOwHT3d5f6S7Edm1AkxM7EzlxXCU3TMHWzcN1yCkD5/ViVfrcXd2Oj24vbMPu3F7dSKBQq+xmF+3+Fg5Pr9pE95n6p3EKkHDzmNeQG9WDWJ//1OmoJsXXrVl555RVefPFFgsEgV199Nc899xyLFy9m+/btXHjhhcyaNYujjz66wyQbOq9SCVCXqCOajnaaaCesBEFPEFvZ1Mbd51dUVAD0SmXJ9mIbSMrLy6lNJMDrPbinO5kcEMvL+6yCZqviYbkiYi3Lt1uGv9wMtDJNd/Y5mzj7fKDrlI0YQW19/eFnny3L/SiArvy8SZXKwW/q1KkEmgP5fbSh41U5gQD2uHHoe/agJRKoQPtJeU+1nvUupTQ365120iSsJEk7kbsIye45lFlvIcShpk6dWugQBgVDN9y93YEKKgIVZJxM/v7ult8T2T7fQ31/t/J5sUeOwCkpQW9qwmxqRum624s7+3dXbuLrODYOTi7xbf2/1slwloaWO6/R/r+jrunomo6Gho6Obui5oqK6pqPrLec03V391fp/Ld+Xer00mTIjP1j1yRVNRy0hwuEwfr8fn8+HYRiUlZXR1NTEO++8w+zZs7nuuut45513+Pjjj/sitHZl7Aw6OhX+irxB54UXXgDgrLPO6rdYCs40UeEwKhwGyxqc1cu7UTyMQCC3/1m17IvOm4E+HL9/wMz8i+HphRdeoDZRy9wvzs077umsOJnHgzNmDPq+fbmaDXk0zX0vdOWji7Kz3j58hL1hlFLYyibjZFpmvBOkW/YcArkK5zLrLcTwNiyvx3pBNqEu8hTl7e+OWTHiGXeFkYbmtnwcwAUtVUezw+rg7HD2PBwyO6wpKPajBU305mY8Te4khOP3oxkGATuAjY2GhmmYecmvjpsQt06agbyEOLtSoHWC3Fs3MTxy83lQ65P/ep21hKiurmbZsmV4PB4mTJjAueeeSzQa5a677uLBBx8kHA5zyy239EVobTjKIeWkGBca12YvS3bp+LAd2NtLupuaCpN0d7V4WDbubAKd3ftsmm0TaLl4F0PAAw88QMpO5RJt27HxGF1cImgYOKNHQyrlvqfAfa9l32+W5X5u9f7TWo5prd+TrXk8EIsdvMhQrWYBssl7y9fukkYdE5OAGQFPMbZyyHAw+U5aSRyc3IWPR/OgD6d9h0IIuR7rBe3t7852koimo8QysVyC2N393a0T4jazw0qRyCSIZ+Ltzg4DubG+PYZmHJwBbpkd1tHzZod19PzZ4fY+l2totoMRjWE0NIDjUBKqpN4Jt/vnCtETfZJod9YSYunSpSxdujTvfFlZGQ8//HBfhNOhuBVnZGBkl/ZwD2vtJd3NzQerl2f3Ix+prhQP83jQkkm38nar/c/KNNuvvC0JtBjGLGUddn92uzTNXZkBh1z6tK/NY7KJefajrAznwIGDCXr2fXxonYPsypPs+77lw7RtTMchgE6EIpQexLItMlik0kniTjNpx0JzFOg6npbWYkrX3b0eWvZzNqnXQD+ymXchhBjqdE3Hb/rxm35KfCXYjk3KSZHIJIhmoiQybs2e7BLpQ/cOZ79unRjrmpvsarqbDBuGkZf8lvhLIEAuOdbQcP+fPzt86PLpXl/irgOlXuziYvc6Np1Gi8XcomnSi1v0omH90xS34oS9YSK+SKFDGVwOTbqTyfyWYS3tp7o8+6zr7uCWbWF1aOuqESOw+2KPthBDkOVY+I1+vHF46PLxdm66dZbAt3u+VfKuK4XPcfApRUQpbNsiY6dIWUkS6TjNmQTKscF2MJWGBwMDDZSDZttoGXf2XYvF0GMxNDg4s94SgaZaH1O0ZOuoXJKutUrgW5J5ubEnhBgiDN0gqAcJmkHKA+VYjkXaTueS6cPOErdaZt2Z8mA5JPrhL9NVuu524iktxTFNtNpatFRqcGyTFIPCsE2003YaUzep8FcUOpTBzTRRoRAqFHKT7lQKrbHRXXJqGG7rqmwC3d3WVf1QoVuIocRjDIELhA72fxuAQQg/UIy7XDHjZMg4GeKZOPFMHEtZuYtBU3OLslllZaTr6vJn2xUtXzuHfN9S46H1DUMF2FbLMeWOd+TfKFAtY5vKjnNCCDEIZYtZDgu67l7LFhW5KzZra90Z7u6u1hSixTB5B7WVslNMiEwYXj0G+1p2b3RRUaEjEWLYUqgBXdSmL2iahtfw4jW8FHnc8cdyLNJO2q24m44Rt+J4015iVtxtddOyj/3Q2fSuLJ/PY9totuMm4JaNlk6jZSz0dBpaOje4s+UAmrsFxpA6EUIIMeBoGioYdDtxJJPodXXQ3OzObvtli6k4csMy0U7baYo8RfiMjsvl33333f0UkRBCdM/dd9/NntgewC3uaGjG8JmF6EB2NiZoBinxlaCUIlwcxpf2EbfiJDIJMiqDpjR0Xc/1mT1ihuHOYuNpf/+6bbvL120HMhm0dBo9nXbbJyqHbBNBzTTdY7ruvp7MhguRI9djol9pGgQCOGPHQjKJ1tCAHo26Y3Pr1mBCdGJYXo1l7AwVgc6XjI8dO7YfohFCiO4bO3YsNLtJtuVYUtjxMDRNw2f6CHlDhLwh4OCsdzKTJG7FiWViucdmW4t1qXr74f/Qgyt9AHCL1NnZ846DZtng2NihMI6uoaXSaFYGLZVqeVCr/eKGnluaLltqxHAi12OiYPx+1OjR2KkUWlMTemOje0M0EJCEW3Rq2CXajnLQNK1LF6NPP/00AOecc05fhyWEEN3y9NNPU5es48wvn4nlWFLc8Qi0nvUuoyzX7iZjZ4hbceJWHNuxc5VwPbqnd1cL6DrKqwMeVDiEbWUOnsvNhrsF3bAttGQKzWpZlu7YgIamFEoDpRlg6Cjd/SwXgGIokesxUXA+H2rECOySEjfhbmhwl5r7/XLjUxzWsEu0U3aKiDfSpVmKRx55BJCBXQgxcD3yyCOk7BRnfvlMFAqvIYVbuqt1u5uwz+2pmnEyZGy3p3frWe/s47MO7f3augJvm3OH9onV3JVWGSeTf04HTTfAYwBeKAoefF3HySXhmu24S9Kze8QTLUXaWlolQqsibdkZcSEGEbkeEwOGx4MqL3dbgzU3o9fXg1K54r9CtDbsEm1b2bllg0IIMdQMt0Jofc2je/DoHoKeIOWU52a903Yay7EAtwBd696yqPxes45y8l7T4eD3TkvrQ0N3+822PnfoY5WTvwvc0R23H6xHR/l9bmLdsjxd2Ra63ZKMWxZkMujpFHo8g+bY4IDScWfEdR30lr3mhlsgrvWNAk/GJGHFc3/X1jcDWn996Dk30T/sP22ejl635UAbJsPwIkYIMTCYJqq0FDsSQYtG3YQ7kXATbunFLVoMq58Ey7Hw6J5Oi6AJIcRgpOG2shJ9p/Wsd28qj5QTyAR67fXcpLvV93k3AhQqk8kValOZtDsTns5AJo2ybTRdc19DQYmnnHqvr2VGXO/yn6M6qOF+6LlDX+fQmxN5Ny5abk6UejykhkIrOyHE4GUYqOJi7HAYLRZzK5VLL27RYlhdkaWdNBX+irw79UIIMRRYysJreGV8EwBtfg7yZog1wHf4JY7unnAbzXJ7hnuKijDTFlomg5bMHPJg7eBydNPs173hJR4PdXo71d6FEKK/6ToqHMYOhdDicbS6OrRo1E24pRf3sDWsEm1HOQQ8vTdjIIQQA4XlWES8UghN9IJsezGv101iy8tRmuZ+3VKkjZYknEwG0mm0VMptT+Y4KKXQdN2dOdf1g6+nS5E2IcQQp2mooiJUMAiJhDvDHY26s9s+WVE73AybRDttpwmawSPav7hq1ao+jEgIIXpu1apV7InuwVGOtPYSfa+lZRm5lmWu3NeOA5aVW5ZOOu32D0+l3JZljoPS3Grp6HrbRFyIdsj1mBh0NA2CQZxswt3QgNbcjMom3HLTcVgYNol2V3tnt1ZeXt5H0QghRO8oLy8n6U0SzUR7t/WUEN2h67llkm0S8exseDYJt6yDs+GZDGQybhKefU42Cc9+lgvTYUuux8SgFgjgBAKQSqE1NqI3Nblbbvx+GdeGuGFxVaZQXe6d3dqaNWsAqK6u7ouwhBCix9asWUN9sp4zzjlDKo6Lga0rs+GHLktPpVpaliXcpei0KkAeDvdn9KKA5HpMDAk+H2rkyPxe3IaB8vlkRc8QNSwS7ZSdosJf0aXe2a098cQTgAzsQoiB64knniBlp1i4aOERj3FCDCi67n54PO0n4tlZ8OyMeCjkVk8XQ55cj4khxetFVVS4CXdjI3pjo3sjMRCQhHuIGRaJtkJJ72whxJAWMKTQoxjisnu5IVekjdragoYkhBDdZpqo8nI34W5udntxO47bi9s4fGcIMXgMi0Q7aAald7YQYkjzG1IITQghhBh0DANVUoIdiaBFo27CnUxKL+4hYMivT9DQKPYVS29ZIUSvcRyHFStWUF1dzfLly9mxY0fe+ZdeeonFixdTXV2dW/KYVVtby/z589m+fXuvxaOj4zHkl7EQQggxaOk6KhLBHj8eZ9Qot26F4xQ6KtEDfTKj7TgON9xwA9u2bcPr9XLzzTczceLE3PlnnnmGhx56CF3XWbx4McuWLSOTyXDttdeya9cudF3nhz/8IVVVVT2OJeQNYWrDYuJeCNFP1q1bRzqdZs2aNWzevJnbbruN++67D4BMJsOtt97Kk08+SSAQYOnSpXzxi19kxIgRZDIZVqxYgd/fu7PPhm7Iqh0hhBBiKNB1VCiEKiqCYBBisUJHJLqpTzLQji5CAX70ox/x7LPPEgwGOfvsszn77LN5/fXXsSyLxx9/nJqaGu68807uueeeHsfSk4vP1atX9/jPF0IMPW+88Qbz5s0DYObMmWzZsiV3bvv27UyYMIHi4mIAZs+ezaZNm/jyl7/M7bffzpIlS3q1J2x2nJJVO0KIoUqux8Sw1NKLm0Si0JGIbuqTRLuji1CAGTNm0NzcjGmaKOW23po8eTK2beM4DtFoFNMs/Cx0MBgsdAhCiAEoGo0SCh0ssGgYBpZlYZom0WiUcKu2Q0VFRUSjUZ566inKysqYN2/eESXahmG06SFrmmbu2EDrL9s6toFGYuuegRrbQI1L9D65HhNCDEZ9ks12dBEKMG3aNBYvXkwgEGDBggVEIhFisRi7du3iy1/+MvX19dx///2d/jmdXYD2VDaGyy67rFdebyBfFEhs3SOxDU+hUIhYq6VcjuPkxrdDz8ViMcLhMKtXr0bTNF577TX+7//+j2uuuYb77ruPESNGdPhn2bZN7SGVlcvLy3PHHn74YQC+8Y1v9MLfrOdaxzbQSGzdM1Bj62pclZWV/RCN6EsDbZwTQoiu6JNEu6OL0K1bt/LKK6/w4osvEgwGufrqq3nuuefYvHkzc+fO5aqrrmL37t1cdNFFrF27Fp/v8Eu/O7sA7anHH38cgPPPP79XXm+gXqyAxNZdgz02uQDtnlmzZvHyyy+zcOFCNm/ezPTp03Pnqqqq2LFjBw0NDQSDQTZt2sQll1zCl770pdxjli9fzg033NBpkt0Va9euBeQCVAgxdMk4J4QYjPok0e7oIjQcDuP3+/H5fBiGQVlZGU1NTUQiETwtJeyLi4uxLAvbtvsiPCGE6JEFCxZQU1PDkiVLUEqxcuVK1q5dSzwep7q6mmuvvZZLLrkEpRSLFy9m1KhRhQ5ZCCGEEEL0oz5JtDu7CK2urmbZsmV4PB4mTJjAueeeSyaT4brrrstVIL/yyitlT44QYkDSdZ2bbrop71jrLgmnn346p59++mGfL4V9hBBCCCGGtj5JtDu7CF26dClLly7NO+/1ernrrrv6IhwhhBBCCCGEEKLf6IUOQAghhBBCCCGEGEoK30NrAPvd735X6BCEEKJDMk4JIYY6GeeEEIORzGgLIYQQQgghhBC9SBJtIYQQQgiRx3EcVqxYQXV1NcuXL2fHjh1tHpNIJFiyZAnbt2/v8nOEEGK4kERbCCGEEELkWbduHel0mjVr1nDVVVdx22235Z1/5513+PrXv87OnTu7/BwhhBhOJNEWQgghhBB53njjDebNmwfAzJkz2bJlS975dDrNvffey5QpU7r8HCGEGE6kGJoQQgghhMgTjUYJhUK57w3DwLIsTNO9dJw9e/YRP0cIIYYTGfmEEEIIIUSeUChELBbLfe84TqcJc3eeA25CXl5ennfMNM02xwYKia17BmpsAzUuGNixic5Joi2EEEIIIfLMmjWLl19+mYULF7J582amT5/eJ88BsG2b2travGPl5eVtjg0UElv3DNTYBmpc0PXYKisr+yEacaQk0RZCCCGEEHkWLFhATU0NS5YsQSnFypUrWbt2LfF4nOrq6i4/RwghhitJtIUQQgghRB5d17npppvyjlVVVbV53OrVqzt8jhBCDFeaUkoVOgghhBBCCCGEEGKokPZeQgghhBBCCCFEL5JEWwghhBBCCCGE6EWSaAshhBBCCCGEEL1IEm0hhBBCCCGEEKIXSaIthBBCCCGEEEL0Ikm0hRBCCCGEEEKIXjRoE23HcVixYgXV1dUsX76cHTt25J1/6aWXWLx4MdXV1TzxxBMDKrZnn32W888/nyVLlrBixQocxxkQcWX94Ac/4I4OrGWcAAAJbklEQVQ77uiXmLoa29tvv82yZctYunQpV1xxBalUasDE9swzz3DuueeyePFiHnvssX6Lq7W33nqL5cuXtzleyPeB6DkZ53o/riwZ544sNhnnRF8ZqOPcQB3juhJbloxzRxZbocc5GeOGIDVI/fnPf1bXXHONUkqpN998U1122WW5c+l0Wp155pmqoaFBpVIptWjRIrVv374BEVsikVBnnHGGisfjSimlrrzySrVu3bqCx5X1m9/8Rl1wwQXqxz/+cb/E1JXYHMdRX/3qV9VHH32klFLqiSeeUNu3bx8QsSml1Jw5c1R9fb1KpVK5n7v+tGrVKvWVr3xFnX/++XnHC/0+ED0n41zvxpUl49yRxaaUjHOi7wzUcW6gjnGdxZYl49yRxaZUYcc5GeOGpkE7o/3GG28wb948AGbOnMmWLVty57Zv386ECRMoLi7G6/Uye/ZsNm3aNCBi83q9PP744wQCAQAsy8Ln8xU8LoA333yTt956i+rq6n6Jp6uxffjhh5SUlPCrX/2Kf/7nf6ahoYEpU6YMiNgAZsyYQXNzM+l0GqUUmqb1W2wAEyZM4J577mlzvNDvA9FzMs71blwg41x3YgMZ50TfGajj3EAd4zqLDWSc605sUNhxTsa4oWnQJtrRaJRQKJT73jAMLMvKnQuHw7lzRUVFRKPRARGbrutUVFQAsHr1auLxOHPmzCl4XPv27eNnP/sZK1as6JdYjiS2+vp63nzzTZYtW8ZDDz3E3/72N1577bUBERvAtGnTWLx4MWeffTannXYakUik32ID+Kd/+idM02xzvNDvA9FzMs71blwyznUvNpBxTvSdgTrODdQxrrPYZJzrXmxQ2HFOxrihadAm2qFQiFgslvvecZzcD+ih52KxWN4PaSFjy35/++23U1NTwz333NNvd8w6iuv555+nvr6eSy+9lFWrVvHss8/y1FNP9UtcncVWUlLCxIkTmTp1Kh6Ph3nz5rW5C1mo2LZu3corr7zCiy++yEsvvURdXR3PPfdcv8XWkUK/D0TPyTjXu3HJONe92GScE31poI5zA3WM6yw2Gee6F9tAHedkjBvcBm2iPWvWLDZs2ADA5s2bmT59eu5cVVUVO3bsoKGhgXQ6zaZNm/jc5z43IGIDWLFiBalUip///Oe5ZUeFjuvCCy/kqaeeYvXq1Vx66aV85StfYdGiRQMitvHjxxOLxXJFKzZt2sS0adMGRGzhcBi/34/P58MwDMrKymhqauq32DpS6PeB6DkZ53o3LhnnuhebjHOiLw3UcW6gjnGdxSbjXPdiG6jjnIxxg1vbNQqDxIIFC6ipqWHJkiUopVi5ciVr164lHo9TXV3NtddeyyWXXIJSisWLFzNq1KgBEdsxxxzDk08+yQknnMBFF10EuIPiggULChpXIfbxHElst9xyC1dddRVKKT73uc9x2mmnDZjYqqurWbZsGR6PhwkTJnDuuef2W2ztGSjvA9FzMs71blwyznU/NhnnRF8ZqOPcQB3jOotNxrnuxzaQxrmB8B4QPacppVShgxBCCCGEEEIIIYaKQbt0XAghhBBCCCGEGIgk0RZCCCGEEEIIIXqRJNpCCCGEEEIIIUQvkkRbCCGEEEIIIYToRZJoCyGEEEIIIYQQvUgS7SHok08+4YILLujSY5966inuuOOOLj3273//O1deeWVPQutVDQ0NrF27FoBVq1bx9ttvFzgiIUR/kXFOCDHUyTgnxOAmibYYtLZt28ZLL70EwKWXXspxxx1X4IiEEKJ3yTgnhBjqZJwTQ5VZ6ABE31q+fDkzZszgvffeIxgMcsIJJ7Bx40aampp48MEHAdi8eTMXXXQR0WiU73znO5x22mk8//zzPProo7nXueuuu/Je99e//jUvvPAClmURDoe55557ePbZZ1m/fj3JZJKPP/6Yb37zmyxatIi33nqLW265BaUUo0aN4o477mDHjh3cfPPNAJSUlLBy5UrC4XDu9e+55x7efPNN4vE4t9xyC3/4wx/YsmULsViMqqoqbr31Vu6//362bt3KmjVrePPNN1m4cCEnn3wy1113HTt37sS2bS6++GIWLlzYD//SQohCkXFOxjkhhjoZ52ScE4OPzGgPA8cddxy/+tWvSKfT+P1+HnroIaZOncrrr78OQCAQ4OGHH2bVqlXcdNNNOI7DRx99xKpVq1i9ejWTJ09m48aNuddzHIeGhgYefvhhHnvsMSzL4p133gEgGo3ywAMPcN9997Fq1SoAfvCDH3Drrbfy29/+lpNPPpnt27fzgx/8gOuvv57Vq1dz6qmn8t///d9t4p4yZQqPP/44o0aNIhKJ8NBDD/H444+zefNm9u7dy2WXXcZJJ51EdXV17jlr1qyhtLSUxx9/nIceeog777yTurq6vvznFUIMADLOyTgnxFAn45yMc2JwkRntYeDoo48GIBKJMHXq1NzXqVQKgNmzZ6NpGuXl5YTDYRoaGigvL+eaa66hqKiIDz74gJkzZ+ZeT9d1PB4P//7v/04wGGTPnj1YlgXAUUcdBUBlZSXpdBqA2tpaqqqqAPj6178OwPbt27nxxhsByGQyTJ48uU3c2WM+n4+6urrcnxePx8lkMu3+Xbdv384pp5wCQCgUoqqqip07d1JWVtbdfz4hxCAg45yMc0IMdTLOyTgnBhdJtEXu7uX+/fuJx+N4PB7uvvtuXnnlFQAuvvhilFK5x2/dupV169bx29/+lkQiwaJFi3LnNU1r8/ojR47ko48+YtKkSaxatYrJkyczefJkbr/9dsaMGcMbb7zB/v372zxP190FFxs2bGD37t25u5l/+ctfUEqh6zqO4+Q9p6qqik2bNrFgwQKi0Sjvvvsu48aN65V/JyHE4CXjnBBiqJNxToiBRRJtQTKZ5MILLyQej3PTTTcRCoWYNWsW5557LsFgkEgkwr59+3ID3MSJEwkEAixatAiv18uIESPYt2/fYV//xhtv5LrrrkPXdUaMGME3vvENKisrueaaa7BtG4BbbrnlsM8/7rjj+PnPf84FF1yA1+tl/Pjx7Nu3jwkTJvDuu+/y8MMP5x57wQUX8IMf/IClS5eSSqW4/PLLKS8v751/KCHEoCXjnBBiqJNxTvz/7dyhEQAwDAOx6/5Dp7S8DwKkLd7A7HLmnbYAAACAL87QAAAAICS0AQAAICS0AQAAICS0AQAAICS0AQAAICS0AQAAICS0AQAAICS0AQAAIHQBQbbOPWrV+CUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_performances_plots(performances_df_balanced_bagging, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Imbalance ratio\",\n",
    "                       summary_performances=summary_performances_balanced_bagging)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show that increasing the imbalance ratio leads to a decrease of both Average Precision and CP@100. The trend is different with AUC ROC, where increasing the imbalance ratio first leads to a slight improvement of the metric, before reaching a plateau. It is worth noting that the results are qualitatively similar to [](Ensembling_Strategies_Transaction_Data_Bagging) with simulated data for AUC ROC and Average Precision. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balanced random forest\n",
    "\n",
    "Similar to [](Ensembling_Strategies_Transaction_Data_RF) with simulated data, the imbalance ratio (`sampling_strategy` parameter) was parametrized to take values in the set $[0.01, 0.05, 0.1, 0.5, 1]$ for the model selection procedure. The number of trees and maximum tree depth were set to 100 and 10 (as with baseline random forest)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC Test</th>\n",
       "      <th>AUC ROC Test Std</th>\n",
       "      <th>Average precision Test</th>\n",
       "      <th>Average precision Test Std</th>\n",
       "      <th>Card Precision@100 Test</th>\n",
       "      <th>Card Precision@100 Test Std</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Execution time</th>\n",
       "      <th>AUC ROC Validation</th>\n",
       "      <th>AUC ROC Validation Std</th>\n",
       "      <th>Average precision Validation</th>\n",
       "      <th>Average precision Validation Std</th>\n",
       "      <th>Card Precision@100 Validation</th>\n",
       "      <th>Card Precision@100 Validation Std</th>\n",
       "      <th>Parameters summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.918636</td>\n",
       "      <td>0.020386</td>\n",
       "      <td>0.093255</td>\n",
       "      <td>0.018644</td>\n",
       "      <td>0.253929</td>\n",
       "      <td>0.026733</td>\n",
       "      <td>{'clf__max_depth': 10, 'clf__n_estimators': 10...</td>\n",
       "      <td>138.678571</td>\n",
       "      <td>0.918772</td>\n",
       "      <td>0.005630</td>\n",
       "      <td>0.099003</td>\n",
       "      <td>0.021238</td>\n",
       "      <td>0.263571</td>\n",
       "      <td>0.031274</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.922445</td>\n",
       "      <td>0.018733</td>\n",
       "      <td>0.092084</td>\n",
       "      <td>0.017847</td>\n",
       "      <td>0.245000</td>\n",
       "      <td>0.025425</td>\n",
       "      <td>{'clf__max_depth': 10, 'clf__n_estimators': 10...</td>\n",
       "      <td>140.741641</td>\n",
       "      <td>0.920488</td>\n",
       "      <td>0.005681</td>\n",
       "      <td>0.094071</td>\n",
       "      <td>0.018980</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.033480</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.925383</td>\n",
       "      <td>0.015810</td>\n",
       "      <td>0.088692</td>\n",
       "      <td>0.017844</td>\n",
       "      <td>0.238929</td>\n",
       "      <td>0.027244</td>\n",
       "      <td>{'clf__max_depth': 10, 'clf__n_estimators': 10...</td>\n",
       "      <td>130.145606</td>\n",
       "      <td>0.921882</td>\n",
       "      <td>0.004630</td>\n",
       "      <td>0.091078</td>\n",
       "      <td>0.019971</td>\n",
       "      <td>0.247500</td>\n",
       "      <td>0.037490</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.926452</td>\n",
       "      <td>0.013919</td>\n",
       "      <td>0.075340</td>\n",
       "      <td>0.013228</td>\n",
       "      <td>0.200714</td>\n",
       "      <td>0.032490</td>\n",
       "      <td>{'clf__max_depth': 10, 'clf__n_estimators': 10...</td>\n",
       "      <td>125.557530</td>\n",
       "      <td>0.921611</td>\n",
       "      <td>0.007349</td>\n",
       "      <td>0.074475</td>\n",
       "      <td>0.016815</td>\n",
       "      <td>0.202857</td>\n",
       "      <td>0.034949</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.924311</td>\n",
       "      <td>0.013693</td>\n",
       "      <td>0.069106</td>\n",
       "      <td>0.011243</td>\n",
       "      <td>0.176786</td>\n",
       "      <td>0.015498</td>\n",
       "      <td>{'clf__max_depth': 10, 'clf__n_estimators': 10...</td>\n",
       "      <td>115.135572</td>\n",
       "      <td>0.919174</td>\n",
       "      <td>0.008283</td>\n",
       "      <td>0.066712</td>\n",
       "      <td>0.015405</td>\n",
       "      <td>0.180714</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AUC ROC Test  AUC ROC Test Std  Average precision Test  \\\n",
       "0      0.918636          0.020386                0.093255   \n",
       "1      0.922445          0.018733                0.092084   \n",
       "2      0.925383          0.015810                0.088692   \n",
       "3      0.926452          0.013919                0.075340   \n",
       "4      0.924311          0.013693                0.069106   \n",
       "\n",
       "   Average precision Test Std  Card Precision@100 Test  \\\n",
       "0                    0.018644                 0.253929   \n",
       "1                    0.017847                 0.245000   \n",
       "2                    0.017844                 0.238929   \n",
       "3                    0.013228                 0.200714   \n",
       "4                    0.011243                 0.176786   \n",
       "\n",
       "   Card Precision@100 Test Std  \\\n",
       "0                     0.026733   \n",
       "1                     0.025425   \n",
       "2                     0.027244   \n",
       "3                     0.032490   \n",
       "4                     0.015498   \n",
       "\n",
       "                                          Parameters  Execution time  \\\n",
       "0  {'clf__max_depth': 10, 'clf__n_estimators': 10...      138.678571   \n",
       "1  {'clf__max_depth': 10, 'clf__n_estimators': 10...      140.741641   \n",
       "2  {'clf__max_depth': 10, 'clf__n_estimators': 10...      130.145606   \n",
       "3  {'clf__max_depth': 10, 'clf__n_estimators': 10...      125.557530   \n",
       "4  {'clf__max_depth': 10, 'clf__n_estimators': 10...      115.135572   \n",
       "\n",
       "   AUC ROC Validation  AUC ROC Validation Std  Average precision Validation  \\\n",
       "0            0.918772                0.005630                      0.099003   \n",
       "1            0.920488                0.005681                      0.094071   \n",
       "2            0.921882                0.004630                      0.091078   \n",
       "3            0.921611                0.007349                      0.074475   \n",
       "4            0.919174                0.008283                      0.066712   \n",
       "\n",
       "   Average precision Validation Std  Card Precision@100 Validation  \\\n",
       "0                          0.021238                       0.263571   \n",
       "1                          0.018980                       0.255000   \n",
       "2                          0.019971                       0.247500   \n",
       "3                          0.016815                       0.202857   \n",
       "4                          0.015405                       0.180714   \n",
       "\n",
       "   Card Precision@100 Validation Std  Parameters summary  \n",
       "0                           0.031274                0.01  \n",
       "1                           0.033480                0.05  \n",
       "2                           0.037490                0.10  \n",
       "3                           0.034949                0.50  \n",
       "4                           0.037109                1.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performances_df_balanced_rf=performances_df_dictionary['Balanced RF']\n",
    "performances_df_balanced_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us summarize the performances to highlight the optimal imbalance ratio, and plot the performances as a function of the imbalance ratio for the three performance metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.922+/-0.0</td>\n",
       "      <td>0.099+/-0.02</td>\n",
       "      <td>0.264+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.925+/-0.02</td>\n",
       "      <td>0.093+/-0.02</td>\n",
       "      <td>0.254+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.926+/-0.01</td>\n",
       "      <td>0.093+/-0.02</td>\n",
       "      <td>0.254+/-0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters           0.1              0.01               0.01\n",
       "Validation performance      0.922+/-0.0      0.099+/-0.02       0.264+/-0.03\n",
       "Test performance           0.925+/-0.02      0.093+/-0.02       0.254+/-0.03\n",
       "Optimal parameter(s)                0.5              0.01               0.01\n",
       "Optimal test performance   0.926+/-0.01      0.093+/-0.02       0.254+/-0.03"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_balanced_rf=get_summary_performances(performances_df_balanced_rf, parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_balanced_rf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_performances_plots(performances_df_balanced_rf, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Imbalance ratio\",\n",
    "                       summary_performances=summary_performances_balanced_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results follow the same trends as with balanced bagging: increasing the imbalance ratio is detrimental to Average Precision and CP@100, but can slightly increase AUC ROC. The results are qualitatively similar to [](Ensembling_Strategies_Transaction_Data_RF) with simulated data for AUC ROC and Average Precision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted XGBoost\n",
    "\n",
    "Finally, similar to [](Ensembling_Strategies_Transaction_Data_Weighted_XGBoost) with simulated data, we varied the `scale_pos_weight` parameter to take values in the set $[1,5,10,50,100]$. The same hyperparameters as the baseline XGBoost were otherwise kept (100 trees with a maximum depth of 6, and a learning rate of 0.1).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC Test</th>\n",
       "      <th>AUC ROC Test Std</th>\n",
       "      <th>Average precision Test</th>\n",
       "      <th>Average precision Test Std</th>\n",
       "      <th>Card Precision@100 Test</th>\n",
       "      <th>Card Precision@100 Test Std</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Execution time</th>\n",
       "      <th>AUC ROC Validation</th>\n",
       "      <th>AUC ROC Validation Std</th>\n",
       "      <th>Average precision Validation</th>\n",
       "      <th>Average precision Validation Std</th>\n",
       "      <th>Card Precision@100 Validation</th>\n",
       "      <th>Card Precision@100 Validation Std</th>\n",
       "      <th>Parameters summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.928408</td>\n",
       "      <td>0.014108</td>\n",
       "      <td>0.094781</td>\n",
       "      <td>0.030425</td>\n",
       "      <td>0.235357</td>\n",
       "      <td>0.068111</td>\n",
       "      <td>{'clf__learning_rate': 0.1, 'clf__max_depth': ...</td>\n",
       "      <td>518.837213</td>\n",
       "      <td>0.924261</td>\n",
       "      <td>0.011350</td>\n",
       "      <td>0.102488</td>\n",
       "      <td>0.019552</td>\n",
       "      <td>0.260714</td>\n",
       "      <td>0.023765</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.927328</td>\n",
       "      <td>0.013481</td>\n",
       "      <td>0.096711</td>\n",
       "      <td>0.025309</td>\n",
       "      <td>0.248929</td>\n",
       "      <td>0.056887</td>\n",
       "      <td>{'clf__learning_rate': 0.1, 'clf__max_depth': ...</td>\n",
       "      <td>626.773925</td>\n",
       "      <td>0.920673</td>\n",
       "      <td>0.016914</td>\n",
       "      <td>0.106036</td>\n",
       "      <td>0.021723</td>\n",
       "      <td>0.268571</td>\n",
       "      <td>0.021213</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.926817</td>\n",
       "      <td>0.013696</td>\n",
       "      <td>0.093330</td>\n",
       "      <td>0.026236</td>\n",
       "      <td>0.231071</td>\n",
       "      <td>0.057821</td>\n",
       "      <td>{'clf__learning_rate': 0.1, 'clf__max_depth': ...</td>\n",
       "      <td>639.092060</td>\n",
       "      <td>0.920256</td>\n",
       "      <td>0.015958</td>\n",
       "      <td>0.098559</td>\n",
       "      <td>0.019405</td>\n",
       "      <td>0.262143</td>\n",
       "      <td>0.021888</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.925220</td>\n",
       "      <td>0.012177</td>\n",
       "      <td>0.095823</td>\n",
       "      <td>0.024201</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.041367</td>\n",
       "      <td>{'clf__learning_rate': 0.1, 'clf__max_depth': ...</td>\n",
       "      <td>627.310905</td>\n",
       "      <td>0.912374</td>\n",
       "      <td>0.027515</td>\n",
       "      <td>0.093519</td>\n",
       "      <td>0.014645</td>\n",
       "      <td>0.244643</td>\n",
       "      <td>0.032739</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.924530</td>\n",
       "      <td>0.012777</td>\n",
       "      <td>0.089102</td>\n",
       "      <td>0.030194</td>\n",
       "      <td>0.237857</td>\n",
       "      <td>0.043910</td>\n",
       "      <td>{'clf__learning_rate': 0.1, 'clf__max_depth': ...</td>\n",
       "      <td>480.403027</td>\n",
       "      <td>0.912332</td>\n",
       "      <td>0.023152</td>\n",
       "      <td>0.086822</td>\n",
       "      <td>0.013398</td>\n",
       "      <td>0.238929</td>\n",
       "      <td>0.039453</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AUC ROC Test  AUC ROC Test Std  Average precision Test  \\\n",
       "0      0.928408          0.014108                0.094781   \n",
       "1      0.927328          0.013481                0.096711   \n",
       "2      0.926817          0.013696                0.093330   \n",
       "3      0.925220          0.012177                0.095823   \n",
       "4      0.924530          0.012777                0.089102   \n",
       "\n",
       "   Average precision Test Std  Card Precision@100 Test  \\\n",
       "0                    0.030425                 0.235357   \n",
       "1                    0.025309                 0.248929   \n",
       "2                    0.026236                 0.231071   \n",
       "3                    0.024201                 0.240000   \n",
       "4                    0.030194                 0.237857   \n",
       "\n",
       "   Card Precision@100 Test Std  \\\n",
       "0                     0.068111   \n",
       "1                     0.056887   \n",
       "2                     0.057821   \n",
       "3                     0.041367   \n",
       "4                     0.043910   \n",
       "\n",
       "                                          Parameters  Execution time  \\\n",
       "0  {'clf__learning_rate': 0.1, 'clf__max_depth': ...      518.837213   \n",
       "1  {'clf__learning_rate': 0.1, 'clf__max_depth': ...      626.773925   \n",
       "2  {'clf__learning_rate': 0.1, 'clf__max_depth': ...      639.092060   \n",
       "3  {'clf__learning_rate': 0.1, 'clf__max_depth': ...      627.310905   \n",
       "4  {'clf__learning_rate': 0.1, 'clf__max_depth': ...      480.403027   \n",
       "\n",
       "   AUC ROC Validation  AUC ROC Validation Std  Average precision Validation  \\\n",
       "0            0.924261                0.011350                      0.102488   \n",
       "1            0.920673                0.016914                      0.106036   \n",
       "2            0.920256                0.015958                      0.098559   \n",
       "3            0.912374                0.027515                      0.093519   \n",
       "4            0.912332                0.023152                      0.086822   \n",
       "\n",
       "   Average precision Validation Std  Card Precision@100 Validation  \\\n",
       "0                          0.019552                       0.260714   \n",
       "1                          0.021723                       0.268571   \n",
       "2                          0.019405                       0.262143   \n",
       "3                          0.014645                       0.244643   \n",
       "4                          0.013398                       0.238929   \n",
       "\n",
       "   Card Precision@100 Validation Std  Parameters summary  \n",
       "0                           0.023765                   1  \n",
       "1                           0.021213                   5  \n",
       "2                           0.021888                  10  \n",
       "3                           0.032739                  50  \n",
       "4                           0.039453                 100  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performances_df_weighted_xgboost=performances_df_dictionary['Weighted XGBoost']\n",
    "performances_df_weighted_xgboost\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us summarize the performances to highlight the optimal imbalance ratio, and plot the performances as a function of the class weight for the three performance metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.924+/-0.01</td>\n",
       "      <td>0.106+/-0.02</td>\n",
       "      <td>0.269+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.928+/-0.01</td>\n",
       "      <td>0.097+/-0.03</td>\n",
       "      <td>0.249+/-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.928+/-0.01</td>\n",
       "      <td>0.097+/-0.03</td>\n",
       "      <td>0.249+/-0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters             1                 5                  5\n",
       "Validation performance     0.924+/-0.01      0.106+/-0.02       0.269+/-0.02\n",
       "Test performance           0.928+/-0.01      0.097+/-0.03       0.249+/-0.06\n",
       "Optimal parameter(s)                  1                 5                  5\n",
       "Optimal test performance   0.928+/-0.01      0.097+/-0.03       0.249+/-0.06"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_weighted_xgboost=get_summary_performances(performances_df_weighted_xgboost, parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_weighted_xgboost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_performances_plots(performances_df_weighted_xgboost, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Class weight\",\n",
    "                       summary_performances=summary_performances_weighted_xgboost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contrary to balanced bagging and balanced random forest, increasing the class weight of the minority class allows to slightly improve the performances in terms of Average Precision and CP@100. Improvements are only observed for a slight increase of the class weight (from 1 to 5). Higher values lead to slight decreases of performances. For AUC ROC, the optimal class weight is found to be 1 (equal cost for the minority and majority classes). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "Let us finally summarize in a single table the results on the real-world dataset. Performance metrics are reported row-wise, while ensemble methods are reported column-wise. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "summary_test_performances = pd.concat([summary_performances_baseline_bagging.iloc[2,:],\n",
    "                                       summary_performances_balanced_bagging.iloc[2,:],\n",
    "                                       summary_performances_baseline_rf.iloc[2,:],\n",
    "                                       summary_performances_balanced_rf.iloc[2,:],\n",
    "                                       summary_performances_baseline_xgboost.iloc[2,:],\n",
    "                                       summary_performances_weighted_xgboost.iloc[2,:],\n",
    "                                      ],axis=1)\n",
    "summary_test_performances.columns=['Baseline Bagging', 'Balanced Bagging', \n",
    "                                   'Baseline RF', 'Balanced RF', \n",
    "                                   'Baseline XGBoost', 'Weighted XGBoost']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Baseline Bagging</th>\n",
       "      <th>Balanced Bagging</th>\n",
       "      <th>Baseline RF</th>\n",
       "      <th>Balanced RF</th>\n",
       "      <th>Baseline XGBoost</th>\n",
       "      <th>Weighted XGBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUC ROC</th>\n",
       "      <td>0.912+/-0.02</td>\n",
       "      <td>0.919+/-0.01</td>\n",
       "      <td>0.912+/-0.02</td>\n",
       "      <td>0.925+/-0.02</td>\n",
       "      <td>0.928+/-0.01</td>\n",
       "      <td>0.928+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average precision</th>\n",
       "      <td>0.072+/-0.03</td>\n",
       "      <td>0.086+/-0.02</td>\n",
       "      <td>0.08+/-0.03</td>\n",
       "      <td>0.093+/-0.02</td>\n",
       "      <td>0.095+/-0.03</td>\n",
       "      <td>0.097+/-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Card Precision@100</th>\n",
       "      <td>0.209+/-0.07</td>\n",
       "      <td>0.248+/-0.04</td>\n",
       "      <td>0.227+/-0.05</td>\n",
       "      <td>0.254+/-0.03</td>\n",
       "      <td>0.235+/-0.07</td>\n",
       "      <td>0.249+/-0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Baseline Bagging Balanced Bagging   Baseline RF  \\\n",
       "AUC ROC                0.912+/-0.02     0.919+/-0.01  0.912+/-0.02   \n",
       "Average precision      0.072+/-0.03     0.086+/-0.02   0.08+/-0.03   \n",
       "Card Precision@100     0.209+/-0.07     0.248+/-0.04  0.227+/-0.05   \n",
       "\n",
       "                     Balanced RF Baseline XGBoost Weighted XGBoost  \n",
       "AUC ROC             0.925+/-0.02     0.928+/-0.01     0.928+/-0.01  \n",
       "Average precision   0.093+/-0.02     0.095+/-0.03     0.097+/-0.03  \n",
       "Card Precision@100  0.254+/-0.03     0.235+/-0.07     0.249+/-0.06  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_test_performances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best improvements were observed for balanced bagging and balanced random forest, for which better performances were obtained compared to baseline bagging and baseline random forest. However, as we noted for the [simulated data](Ensembling_Strategies_Transaction_Data), the benefits of resampling are most likely due to a higher diversity of the trees making up the ensembles, leading to a decrease of the overfitting phenomenon. In particular, the optimum imbalance ratio for Average Precision and CP@100 was found to be the lowest one (0.01), which shows that the best strategy for these metrics was to avoid rebalancing the training sets.\n",
    "\n",
    "On the contrary, we observed that rebalancing the training sets could slightly improve the performances in terms of AUC ROC. The improvements were observed for imbalance ratios ranging from 0.1 to 0.5, leading to a slight increase of around 1% of the AUC ROC (from 0.91 to 0.92). Besides allowing a slight increase in performances, it is worth noting that rebalancing the dataset with undersampling techniques could speed up computation times by up to 20%.\n",
    "\n",
    "As for the results obtained on [simulated data](Ensembling_Strategies_Transaction_Data_Summary), these experiments suggest that rebalancing can help improve performances in terms of AUC ROC or speed up the training time of an ensemble. It however appeared that keeping all of the training data was the best strategy if Average Precision and CP@100 are the performance metrics to optimize.\n",
    "\n",
    "Overall, the best performances were obtained with XGBoost for the three metrics. As for the [simulated data](Ensembling_Strategies_Transaction_Data_Summary), modifying the class weight through weighted XGBoost did not allow to significantly improve performances, illustrating the robustness of XGBoost in class imbalance scenarios."
   ]
  }
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